[C++/Python][Dataset] Support schema evolution for integer columns

[asfimport]

When reading in a dataset where the schema specifies that column X is of type int64 but the partition actually contains the data stored in that columns as int32, an upcast should be done.

Reporter: Uwe Korn / @xhochy

Related issues:

• [C++][Dataset] Untangle Dataset, Fragment and ScanOptions (is blocked by)
• [C++][Dataset] Schema evolution in Dataset scanning (is related to)

_{Note: This issue was originally created as ARROW-8282. Please see the migration documentation for further details.}

[asfimport]

Francois Saint-Jacques / @fsaintjacques:
Once we have instanciated Fragment, we can create a CastFragment composing an existing Fragment. Some format do support casting, e.g. CSV, and some don't e.g. Parquet or IPC.

[asfimport]

Joris Van den Bossche / @jorisvandenbossche:
Do we need a separate Fragment type? We could also do the cast when scanning (eg we already do some edits at that point, like projection, adding null columns, etc)

cc @bkietz

[asfimport]

Antoine Pitrou / @pitrou:
@jorisvandenbossche Does this still need doing?

[asfimport]

Uwe Korn / @xhochy:
This is still an issue especially in my context, I can have a look at that in the next two weeks.

[asfimport]

Uwe Korn / @xhochy:
This has been resolved on master in the meantime thus this will work starting with the 4.0 release.