Talk about the result type for coerced type

[liukun4515]

Is your feature request related to a problem or challenge? Please describe what you are trying to do.
In the #122 and #1356, I add new datatype(decimal) and propose a more clear coercion rule for expr system.
When I add decimal to the SUM and AVG function #1408 , I meet some problem.
It's about the returned data type.

For example:
https://github.com/apache/arrow-datafusion/blob/415c5e124af18a05500514f78604366d860dcf5a/datafusion/src/physical_plan/expressions/sum.rs#L49 for floating-point datatype, the pg use the double-precision(double/float64) as the returned data type, but the datafusion is different.

There may be other different rules in spark

Which rule should we follow? For example sum and avg?
@alamb @houqp

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[alamb]

@liukun4515 -- I think the rationale for using float64 for the result of sum(float32) and avg(float32) is done to minimize error due to floating point rounding

I think using float64 for the result type makes sense

[liukun4515]

I think using float64 for the result type makes sense

Do you mean to change this line https://github.com/apache/arrow-datafusion/blob/415c5e124af18a05500514f78604366d860dcf5a/datafusion/src/physical_plan/expressions/sum.rs#L57 and return FLOAT64 for sum(float32)?