[SPARK-40535][SQL] Fix bug the buffer of AggregatingAccumulator will not be created if the input rows is empty

[beliefer]

What changes were proposed in this pull request?

When AggregatingAccumulator serialize aggregate buffer, may throwing NPE.
There is one test case could repeat this error.

val namedObservation = Observation("named")
val df = spark.range(1, 10, 1, 10)
val observed_df = df.observe(
  namedObservation, percentile_approx($"id", lit(0.5), lit(100)).as("percentile_approx_val"))
observed_df.collect()

throws exception as follows:

13:45:10.976 ERROR org.apache.spark.util.Utils: Exception encountered
java.lang.NullPointerException
	at org.apache.spark.sql.catalyst.expressions.aggregate.TypedImperativeAggregate.getBufferObject(interfaces.scala:641)
	at org.apache.spark.sql.catalyst.expressions.aggregate.TypedImperativeAggregate.getBufferObject(interfaces.scala:602)
	at org.apache.spark.sql.catalyst.expressions.aggregate.TypedImperativeAggregate.serializeAggregateBufferInPlace(interfaces.scala:624)
	at org.apache.spark.sql.execution.AggregatingAccumulator.withBufferSerialized(AggregatingAccumulator.scala:205)
	at org.apache.spark.sql.execution.AggregatingAccumulator.withBufferSerialized(AggregatingAccumulator.scala:33)
	at org.apache.spark.util.AccumulatorV2.writeReplace(AccumulatorV2.scala:186)
	at sun.reflect.GeneratedMethodAccessor3.invoke(Unknown Source)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:498)
	at java.io.ObjectStreamClass.invokeWriteReplace(ObjectStreamClass.java:1245)
	at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1136)
	at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:348)
	at org.apache.spark.scheduler.DirectTaskResult.$anonfun$writeExternal$2(TaskResult.scala:55)
	at org.apache.spark.scheduler.DirectTaskResult.$anonfun$writeExternal$2$adapted(TaskResult.scala:55)
	at scala.collection.Iterator.foreach(Iterator.scala:943)
	at scala.collection.Iterator.foreach$(Iterator.scala:943)
	at scala.collection.AbstractIterator.foreach(Iterator.scala:1431)
	at scala.collection.IterableLike.foreach(IterableLike.scala:74)
	at scala.collection.IterableLike.foreach$(IterableLike.scala:73)
	at scala.collection.AbstractIterable.foreach(Iterable.scala:56)
	at org.apache.spark.scheduler.DirectTaskResult.$anonfun$writeExternal$1(TaskResult.scala:55)
	at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
	at org.apache.spark.util.Utils$.tryOrIOException(Utils.scala:1456)
	at org.apache.spark.scheduler.DirectTaskResult.writeExternal(TaskResult.scala:51)
	at java.io.ObjectOutputStream.writeExternalData(ObjectOutputStream.java:1459)
	at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1430)
	at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
	at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:348)
	at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:46)
	at org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:115)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:663)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:748)

Why are the changes needed?

Fix a bug.
After my investigation, The root cause is the buffer of AggregatingAccumulator will not be created if the input rows is empty.

Does this PR introduce any user-facing change?

'Yes'.
Users will see the correct results.

How was this patch tested?

New test case.

[beliefer]

ping @MaxGekk cc @cloud-fan

[MaxGekk]

LGTM, I have checked the example from SPARK-40535:

scala> import org.apache.spark.sql.functions._
import org.apache.spark.sql.functions._

scala> val df = spark.range(1,10,1,11)
df: org.apache.spark.sql.Dataset[Long] = [id: bigint]

scala> df.observe("collectedList", collect_list("id")).collect()
res0: Array[Long] = Array(1, 2, 3, 4, 5, 6, 7, 8, 9)

Waiting for CI.

[MaxGekk]

+1, LGTM. Merging to master/3.3.
Thank you, @beliefer.

[beliefer]

@MaxGekk Thank you for you ping. Let me have a chance to fix my bug.