[Java] Spark job fails due to arrow buf limitation

Hello,

Groupby + applyinPandas results in following error. We need some parameter to tune buffer size.

 
```java

Caused by: java.lang.IndexOutOfBoundsException: index: 0, length: 1073741824 (expected: range(0, 0)) at io.netty.buffer.ArrowBuf.checkIndex(ArrowBuf.java:716) at io.netty.buffer.ArrowBuf.setBytes(ArrowBuf.java:954) at org.apache.arrow.vector.BaseVariableWidthVector.reallocDataBuffer(BaseVariableWidthVector.java:508) at org.apache.arrow.vector.BaseVariableWidthVector.handleSafe(BaseVariableWidthVector.java:1239) at org.apache.arrow.vector.BaseVariableWidthVector.setSafe(BaseVariableWidthVector.java:1066) at org.apache.spark.sql.execution.arrow.StringWriter.setValue(ArrowWriter.scala:287) at org.apache.spark.sql.execution.arrow.ArrowFieldWriter.write(ArrowWriter.scala:151) at org.apache.spark.sql.execution.arrow.ArrowWriter.write(ArrowWriter.scala:105) at org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.$anonfun$writeIteratorToStream$1(ArrowPythonRunner.scala:100) at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1581) at org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.writeIteratorToStream(ArrowPythonRunner.scala:122) at org.apache.spark.api.python.BasePythonRunner$WriterThread.$anonfun$run$1(PythonRunner.scala:478) at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:2146) at org.apache.spark.api.python.BasePythonRunner$WriterThread.run(PythonRunner.scala:270)
```

**Reporter**: [Shubham Chhabra](https://issues.apache.org/jira/browse/ARROW-15983)

_{**Note**: *This issue was originally created as [ARROW-15983](https://issues.apache.org/jira/browse/ARROW-15983). Please see the [migration documentation](https://github.com/apache/arrow/issues/14542) for further details.*}

La trace pointe vers ArrowBuf.java, BaseVariableWidthVector, ArrowWriter.scala de Spark et ArrowPythonRunner.scala ; commencez par reproduire le cas groupby + applyInPandas et suivre la limite du buffer. C’est terminé lorsque l’échec signalé est corrigé avec le comportement de buffer configurable demandé et une couverture pour ce chemin.

Rédigé par le modèle d'indexation à partir du texte de l'issue.