[MINOR] Revert removing explicit typing (changed in some examples and StatFunctions)

## What changes were proposed in this pull request?

This PR reverts some changes in https://github.com/apache/spark/pull/12413. (please see the discussion in that PR).

from
```scala
    words.foreachRDD { (rdd, time) =>
    ...
```

to
```scala
    words.foreachRDD { (rdd: RDD[String], time: Time) =>
    ...
```

Also, this was discussed in dev-mailing list, [here](http://apache-spark-developers-list.1001551.n3.nabble.com/Question-about-Scala-style-explicit-typing-within-transformation-functions-and-anonymous-val-td17173.html)

## How was this patch tested?

This was tested with `sbt scalastyle`.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #12452 from HyukjinKwon/revert-explicit-typing.

examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala

@@ -116,7 +116,7 @@ object RecoverableNetworkWordCount {
     val lines = ssc.socketTextStream(ip, port)
     val words = lines.flatMap(_.split(" "))
     val wordCounts = words.map((_, 1)).reduceByKey(_ + _)
-    wordCounts.foreachRDD { (rdd, time) =>
+    wordCounts.foreachRDD { (rdd: RDD[(String, Int)], time: Time) =>
       // Get or register the blacklist Broadcast
       val blacklist = WordBlacklist.getInstance(rdd.sparkContext)
       // Get or register the droppedWordsCounter Accumulator

examples/src/main/scala/org/apache/spark/examples/streaming/SqlNetworkWordCount.scala

@@ -59,7 +59,7 @@ object SqlNetworkWordCount {
     val words = lines.flatMap(_.split(" "))
 
     // Convert RDDs of the words DStream to DataFrame and run SQL query
-    words.foreachRDD { (rdd, time) =>
+    words.foreachRDD { (rdd: RDD[String], time: Time) =>
       // Get the singleton instance of SQLContext
       val sqlContext = SQLContextSingleton.getInstance(rdd.sparkContext)
       import sqlContext.implicits._

sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala

@@ -431,7 +431,7 @@ private[sql] object StatFunctions extends Logging {
       s"exceed 1e4. Currently $columnSize")
     val table = counts.groupBy(_.get(0)).map { case (col1Item, rows) =>
       val countsRow = new GenericMutableRow(columnSize + 1)
-      rows.foreach { row =>
+      rows.foreach { (row: Row) =>
         // row.get(0) is column 1
         // row.get(1) is column 2
         // row.get(2) is the frequency