[SPARK-30082][SQL] Depend on Scala type coercion when building replace query

[johnhany97]

apache#26738
apache#26749

What changes were proposed in this pull request?

Depend on type coercion when building the replace query. This would solve an edge case where when trying to replace NaNs, 0s would get replace too.

Why are the changes needed?

This Scala code snippet:

import scala.math;

println(Double.NaN.toLong)

returns 0 which is problematic as if you run the following Spark code, 0s get replaced as well:

>>> df = spark.createDataFrame([(1.0, 0), (0.0, 3), (float('nan'), 0)], ("index", "value"))
>>> df.show()
+-----+-----+
|index|value|
+-----+-----+
|  1.0|    0|
|  0.0|    3|
|  NaN|    0|
+-----+-----+
>>> df.replace(float('nan'), 2).show()
+-----+-----+
|index|value|
+-----+-----+
|  1.0|    2|
|  0.0|    3|
|  2.0|    2|
+-----+-----+ 

Does this PR introduce any user-facing change?

Yes, after the PR, running the same above code snippet returns the correct expected results:

>>> df = spark.createDataFrame([(1.0, 0), (0.0, 3), (float('nan'), 0)], ("index", "value"))
>>> df.show()
+-----+-----+
|index|value|
+-----+-----+
|  1.0|    0|
|  0.0|    3|
|  NaN|    0|
+-----+-----+

>>> df.replace(float('nan'), 2).show()
+-----+-----+
|index|value|
+-----+-----+
|  1.0|    0|
|  0.0|    3|
|  2.0|    0|
+-----+-----+

And additionally, query results are changed as a result of the change in depending on scala's type coercion rules.

How was this patch tested?

Added unit tests to verify replacing NaN only affects columns of type Float and Double.

[palantirtech]

Thanks for your interest in palantir/spark, @johnhany97! Before we can accept your pull request, you need to sign our contributor license agreement - just visit https://cla.palantir.com/ and follow the instructions. Once you sign, I'll automatically update this pull request.

[johnhany97]

@mccheah, can you take a look?

[mccheah]

Approving as this is a cherry-pick