[SPARK-33248][SQL] Add a configuration to control the legacy behavior of whether need to pad null value when value size less then schema size

### What changes were proposed in this pull request?
Add a configuration to control the legacy behavior of whether need to pad null value when value size less then schema size.
Since we can't decide whether it's a but and some use need it behavior same as Hive.

### Why are the changes needed?
Provides a compatible choice between historical behavior and Hive

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
Existed UT

Closes #30156 from AngersZhuuuu/SPARK-33284.

Lead-authored-by: angerszhu <angers.zhu@gmail.com>
Co-authored-by: AngersZhuuuu <angers.zhu@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>

docs/sql-migration-guide.md

@@ -52,6 +52,8 @@ license: |
   
   - In Spark 3.1, the `schema_of_json` and `schema_of_csv` functions return the schema in the SQL format in which field names are quoted. In Spark 3.0, the function returns a catalog string without field quoting and in lower case. 
  
+  - In Spark 3.1, when `spark.sql.legacy.transformationPadNullWhenValueLessThenSchema` is true, Spark will pad NULL value when script transformation's output value size less then schema size in default-serde mode(script transformation with row format of `ROW FORMAT DELIMITED`). If false, Spark will keep original behavior to throw `ArrayIndexOutOfBoundsException`.
+
 ## Upgrading from Spark SQL 3.0 to 3.0.1
 
 - In Spark 3.0, JSON datasource and JSON function `schema_of_json` infer TimestampType from string values if they match to the pattern defined by the JSON option `timestampFormat`. Since version 3.0.1, the timestamp type inference is disabled by default. Set the JSON option `inferTimestamp` to `true` to enable such type inference.

sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala

@@ -2765,6 +2765,18 @@ object SQLConf {
       .checkValue(_ > 0, "The timeout value must be positive")
       .createWithDefault(10L)
 
+  val LEGACY_SCRIPT_TRANSFORM_PAD_NULL =
+    buildConf("spark.sql.legacy.transformationPadNullWhenValueLessThenSchema")
+      .internal()
+      .doc("Whether pad null value when transformation output's value size less then " +
+        "schema size in default-serde mode(script transformation with row format of " +
+        "`ROW FORMAT DELIMITED`)." +
+        "When true, Spark will pad NULL value to keep same behavior with hive." +
+        "When false, Spark keep original behavior to throw `ArrayIndexOutOfBoundsException`")
+      .version("3.1.0")
+      .booleanConf
+      .createWithDefault(true)
+
   val LEGACY_ALLOW_CAST_NUMERIC_TO_TIMESTAMP =
     buildConf("spark.sql.legacy.allowCastNumericToTimestamp")
       .internal()
@@ -3493,6 +3505,9 @@ class SQLConf extends Serializable with Logging {
   def legacyAllowModifyActiveSession: Boolean =
     getConf(StaticSQLConf.LEGACY_ALLOW_MODIFY_ACTIVE_SESSION)
 
+  def legacyPadNullWhenValueLessThenSchema: Boolean =
+    getConf(SQLConf.LEGACY_SCRIPT_TRANSFORM_PAD_NULL)
+
   def legacyAllowCastNumericToTimestamp: Boolean =
     getConf(SQLConf.LEGACY_ALLOW_CAST_NUMERIC_TO_TIMESTAMP)
 

sql/core/src/main/scala/org/apache/spark/sql/execution/BaseScriptTransformationExec.scala

@@ -104,10 +104,16 @@ trait BaseScriptTransformationExec extends UnaryExecNode {
       val reader = new BufferedReader(new InputStreamReader(inputStream, StandardCharsets.UTF_8))
 
       val outputRowFormat = ioschema.outputRowFormatMap("TOK_TABLEROWFORMATFIELD")
+
+      val padNull = if (conf.legacyPadNullWhenValueLessThenSchema) {
+        (arr: Array[String], size: Int) => arr.padTo(size, null)
+      } else {
+        (arr: Array[String], size: Int) => arr
+      }
       val processRowWithoutSerde = if (!ioschema.schemaLess) {
         prevLine: String =>
           new GenericInternalRow(
-            prevLine.split(outputRowFormat).padTo(outputFieldWriters.size, null)
+            padNull(prevLine.split(outputRowFormat), outputFieldWriters.size)
               .zip(outputFieldWriters)
               .map { case (data, writer) => writer(data) })
       } else {
@@ -118,7 +124,7 @@ trait BaseScriptTransformationExec extends UnaryExecNode {
         val kvWriter = CatalystTypeConverters.createToCatalystConverter(StringType)
         prevLine: String =>
           new GenericInternalRow(
-            prevLine.split(outputRowFormat).slice(0, 2).padTo(2, null)
+            padNull(prevLine.split(outputRowFormat).slice(0, 2), 2)
               .map(kvWriter))
       }