[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.*}

Der Trace verweist auf ArrowBuf.java, BaseVariableWidthVector, Sparks ArrowWriter.scala und ArrowPythonRunner.scala; beginne damit, den Fall groupby + applyInPandas zu reproduzieren und die Buffer-Grenze nachzuverfolgen. Fertig bedeutet, dass der gemeldete Fehler mit dem angeforderten steuerbaren Buffer-Verhalten und einer Abdeckung für diesen Pfad behoben ist.

Vom Indexierungsmodell aus dem Issue-Text verfasst.