Resolve issues with ambiguity in typed Series

[Dr-Irv]

pyright and mypy have issues in dealing with overloads on generics that are ambiguous, as pointed out by numpy. See the following:

• numpy issue: BUG: NDArray[Any] methods ignore overload ambiguity numpy/numpy#20099
• pyright discussion: Pyright does not handle overload ambiguity microsoft/pyright#2521
• mypy issue: Overload ambiguity is ignored in self-annotated methods python/mypy#11347

Issue comes in to play with respect to subtraction. For pandas, we'd like an untyped Series to remain untyped, but subtraction of two Series[Timestamp] to yield Series[Timedelta] . This doesn't seem possible. Right now, the current stubs return Series[Timestamp] when subtracting two untyped series, but do return Series[Timedelta] when subtracting two Series[Timestamp]. Discovered this by using the new assert_type() feature.

In the current stubs from MS copied here, we use Series[bool], Series[Timestamp], Series[Timedelta], Series[float] and Series[int] as arguments and/or return types of different methods, which sharpens up some of the type checks.

Possible solutions:

1. Give up on using generic Series.
2. For typing purposes, create BoolSeries, TimestampSeries, TimedeltaSeries, FloatSeries and IntSeries that are typing subclasses of Series that can help with series that have types and those that don't.

Need to experiment with (2), and if it can't work, just remove all the generic stuff.

[Dr-Irv]

Latest commit #12250f0de83ba445507dc301a62c46f659f0a6aa has a start in using (2) above which seems to be a promising solution.

[Dr-Irv]

Closing - now seems to work by creating special classes for Series of Timestamp and Series of Timedelta