[SPARK-31256][SQL] DataFrameNaFunctions.drop should work for nested columns

### What changes were proposed in this pull request?

#26700 removed the ability to drop a row whose nested column value is null.

For example, for the following `df`:
```
val schema = new StructType()
  .add("c1", new StructType()
    .add("c1-1", StringType)
    .add("c1-2", StringType))
val data = Seq(Row(Row(null, "a2")), Row(Row("b1", "b2")), Row(null))
val df = spark.createDataFrame(spark.sparkContext.parallelize(data), schema)
df.show
+--------+
|      c1|
+--------+
|  [, a2]|
|[b1, b2]|
|    null|
+--------+
```
In Spark 2.4.4,
```
df.na.drop("any", Seq("c1.c1-1")).show
+--------+
|      c1|
+--------+
|[b1, b2]|
+--------+
```
In Spark 2.4.5 or Spark 3.0.0-preview2, if nested columns are specified, they are ignored.
```
df.na.drop("any", Seq("c1.c1-1")).show
+--------+
|      c1|
+--------+
|  [, a2]|
|[b1, b2]|
|    null|
+--------+
```
### Why are the changes needed?

This seems like a regression.

### Does this PR introduce any user-facing change?

Now, the nested column can be specified:
```
df.na.drop("any", Seq("c1.c1-1")).show
+--------+
|      c1|
+--------+
|[b1, b2]|
+--------+
```

Also, if `*` is specified as a column, it will throw an `AnalysisException` that `*` cannot be resolved, which was the behavior in 2.4.4. Currently, in master, it has no effect.

### How was this patch tested?

Updated existing tests.

Closes #28266 from imback82/SPARK-31256.

Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>

sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala

@@ -89,7 +89,7 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
    * @since 1.3.1
    */
   def drop(how: String, cols: Seq[String]): DataFrame = {
-    drop0(how, toAttributes(cols))
+    drop0(how, cols.map(df.resolve(_)))
   }
 
   /**
@@ -115,7 +115,7 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
    * @since 1.3.1
    */
   def drop(minNonNulls: Int, cols: Seq[String]): DataFrame = {
-    drop0(minNonNulls, toAttributes(cols))
+    drop0(minNonNulls, cols.map(df.resolve(_)))
   }
 
   /**
@@ -480,7 +480,7 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
     df.queryExecution.analyzed.output
   }
 
-  private def drop0(how: String, cols: Seq[Attribute]): DataFrame = {
+  private def drop0(how: String, cols: Seq[NamedExpression]): DataFrame = {
     how.toLowerCase(Locale.ROOT) match {
       case "any" => drop0(cols.size, cols)
       case "all" => drop0(1, cols)
@@ -488,12 +488,10 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
     }
   }
 
-  private def drop0(minNonNulls: Int, cols: Seq[Attribute]): DataFrame = {
+  private def drop0(minNonNulls: Int, cols: Seq[NamedExpression]): DataFrame = {
     // Filtering condition:
     // only keep the row if it has at least `minNonNulls` non-null and non-NaN values.
-    val predicate = AtLeastNNonNulls(
-      minNonNulls,
-      outputAttributes.filter{ col => cols.exists(_.semanticEquals(col)) })
+    val predicate = AtLeastNNonNulls(minNonNulls, cols)
     df.filter(Column(predicate))
   }
 

sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala

@@ -45,6 +45,16 @@ class DataFrameNaFunctionsSuite extends QueryTest with SharedSparkSession {
     ).toDF("int", "long", "short", "byte", "float", "double")
   }
 
+  def createDFWithNestedColumns: DataFrame = {
+    val schema = new StructType()
+      .add("c1", new StructType()
+        .add("c1-1", StringType)
+        .add("c1-2", StringType))
+    val data = Seq(Row(Row(null, "a2")), Row(Row("b1", "b2")), Row(null))
+    spark.createDataFrame(
+      spark.sparkContext.parallelize(data), schema)
+  }
+
   test("drop") {
     val input = createDF()
     val rows = input.collect()
@@ -275,33 +285,35 @@ class DataFrameNaFunctionsSuite extends QueryTest with SharedSparkSession {
     assert(message.contains("Reference 'f2' is ambiguous"))
   }
 
-  test("fill/drop with col(*)") {
+  test("fill with col(*)") {
     val df = createDF()
     // If columns are specified with "*", they are ignored.
     checkAnswer(df.na.fill("new name", Seq("*")), df.collect())
-    checkAnswer(df.na.drop("any", Seq("*")), df.collect())
   }
 
-  test("fill/drop with nested columns") {
-    val schema = new StructType()
-      .add("c1", new StructType()
-        .add("c1-1", StringType)
-        .add("c1-2", StringType))
+  test("drop with col(*)") {
+    val df = createDF()
+    val exception = intercept[AnalysisException] {
+      df.na.drop("any", Seq("*"))
+    }
+    assert(exception.getMessage.contains("Cannot resolve column name \"*\""))
+  }
 
-    val data = Seq(
-      Row(Row(null, "a2")),
-      Row(Row("b1", "b2")),
-      Row(null))
+  test("fill with nested columns") {
+    val df = createDFWithNestedColumns
 
-    val df = spark.createDataFrame(
-      spark.sparkContext.parallelize(data), schema)
+    // Nested columns are ignored for fill().
+    checkAnswer(df.na.fill("a1", Seq("c1.c1-1")), df)
+  }
 
-    checkAnswer(df.select("c1.c1-1"),
-      Row(null) :: Row("b1") :: Row(null) :: Nil)
+  test("drop with nested columns") {
+    val df = createDFWithNestedColumns
 
-    // Nested columns are ignored for fill() and drop().
-    checkAnswer(df.na.fill("a1", Seq("c1.c1-1")), data)
-    checkAnswer(df.na.drop("any", Seq("c1.c1-1")), data)
+    // Rows with the specified nested columns whose null values are dropped.
+    assert(df.count == 3)
+    checkAnswer(
+      df.na.drop("any", Seq("c1.c1-1")),
+      Seq(Row(Row("b1", "b2"))))
   }
 
   test("replace") {