[SPARK-51316][PYTHON][FOLLOW-UP] Revert unrelated changes and mark ma…

…pInPandas/mapInArrow batched in byte size

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

This PR is a followup of #50096 that reverts unrelated changes and mark mapInPandas/mapInArrow batched in byte size

### Why are the changes needed?

To make the original change self-contained, and mark mapInPandas/mapInArrow batched in byte size to be consistent.

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

No, the main change has not been released out yet.

### How was this patch tested?

Manually.

### Was this patch authored or co-authored using generative AI tooling?

No.

Closes #50111 from HyukjinKwon/SPARK-51316-followup.

Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>

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

@@ -36,6 +36,7 @@ import org.apache.spark.sql.catalyst.plans.ReferenceAllColumns
 import org.apache.spark.sql.catalyst.plans.logical.{EventTimeWatermark, FunctionUtils, LogicalGroupState}
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.catalyst.types.DataTypeUtils
+import org.apache.spark.sql.execution.python.BatchIterator
 import org.apache.spark.sql.execution.r.ArrowRRunner
 import org.apache.spark.sql.execution.streaming.GroupStateImpl
 import org.apache.spark.sql.internal.SQLConf
@@ -218,13 +219,17 @@ case class MapPartitionsInRWithArrowExec(
     child: SparkPlan) extends UnaryExecNode {
   override def producedAttributes: AttributeSet = AttributeSet(output)
 
+  private val batchSize = conf.arrowMaxRecordsPerBatch
+
   override def outputPartitioning: Partitioning = child.outputPartitioning
 
   override protected def doExecute(): RDD[InternalRow] = {
     child.execute().mapPartitionsInternal { inputIter =>
       val outputTypes = schema.map(_.dataType)
 
-      val batchIter = Iterator(inputIter)
+      // DO NOT use iter.grouped(). See BatchIterator.
+      val batchIter =
+        if (batchSize > 0) new BatchIterator(inputIter, batchSize) else Iterator(inputIter)
 
       val runner = new ArrowRRunner(func, packageNames, broadcastVars, inputSchema,
         SQLConf.get.sessionLocalTimeZone, RRunnerModes.DATAFRAME_DAPPLY)

sql/core/src/main/scala/org/apache/spark/sql/execution/python/MapInBatchEvaluatorFactory.scala

@@ -69,7 +69,7 @@ class MapInBatchEvaluatorFactory(
         pythonRunnerConf,
         pythonMetrics,
         jobArtifactUUID,
-        None)
+        None) with BatchedPythonArrowInput
       val columnarBatchIter = pyRunner.compute(batchIter, context.partitionId(), context)
 
       val unsafeProj = UnsafeProjection.create(output, output)

sql/core/src/main/scala/org/apache/spark/sql/execution/python/PythonArrowInput.scala

@@ -145,7 +145,10 @@ private[python] trait BasicPythonArrowInput extends PythonArrowInput[Iterator[In
 
 private[python] trait BatchedPythonArrowInput extends BasicPythonArrowInput {
   self: BasePythonRunner[Iterator[InternalRow], _] =>
-  private val arrowMaxRecordsPerBatch = SQLConf.get.arrowMaxRecordsPerBatch
+  private val arrowMaxRecordsPerBatch = {
+    val v = SQLConf.get.arrowMaxRecordsPerBatch
+    if (v > 0) v else Int.MaxValue
+  }
 
   private val maxBytesPerBatch = SQLConf.get.arrowMaxBytesPerBatch