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_

Comienza con la discusión en el pull request 6832 y la documentación de AnalyzerRule enlazada en la issue. Rastrea cómo se analiza la división decimal y determina después dónde deben ubicarse una nueva opción de configuración y la reescritura de Decimal256 a Decimal128. Se considera terminado cuando el comportamiento compatible con Spark de la división decimal está cubierto por pruebas, aunque en la issue no se nombra ningún archivo de prueba.

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