Decimal division compatibility mode with spark

### Is your feature request related to a problem or challenge?

As described in detail by @liukun4515 and @tustvold and @viirya on https://github.com/apache/arrow-datafusion/pull/6832, DataFusion's decimal devision semantics.

@liukun4515 notes https://github.com/apache/arrow-datafusion/pull/6832#issuecomment-1680098056 that spark has the config to control the precision loss : https://github.com/apache/spark/blob/2be20e54a2222f6cdf64e8486d1910133b43665f/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala#L246

And @tustvold notes For people looking to emulate spark which only supports precision up to 38, casting to Decimal256 and then truncating down to Decimal128 will be equivalent, and is what a precision loss arithmetic kernel would do

### Describe the solution you'd like

If anyone needs spark compatible decimal division rules, I suggest:

1. Add a new config option
2. Apply the rewrite suggested by @tustvold (cast to Decimal256, divide, and then cast to Decimal128) as an [AnalyzerRule](https://docs.rs/datafusion/latest/datafusion/optimizer/analyzer/trait.AnalyzerRule.html#)

### Describe alternatives you've considered

See ticket -- we discussed at length changing the semantics of division in arrow-rs and concluded there was no one agreed upon ideal behavior

### Additional context

_No response_

Beginne mit der Diskussion in Pull Request 6832 und der in der Issue verlinkten Dokumentation zu AnalyzerRule. Verfolge, wie die Division von Dezimalzahlen analysiert wird, und ermittle dann, wo eine neue Konfigurationsoption und die Umschreibung von Decimal256 in Decimal128 hingehören. Als erledigt gilt die Aufgabe, wenn das Spark-kompatible Verhalten bei der Division von Dezimalzahlen durch Tests abgedeckt ist, obwohl in der Issue keine Testdatei genannt wird.

Vom Indexierungsmodell aus dem Issue-Text verfasst.