Fix select_dtypes(include='int') for Windows.
- [x] closes #36596
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry

This pull request is stale because it has been open for thirty days with no activity. Please update or respond to this comment if you're still interested in working on this. @jreback 
What I see as possible changes to `.select_dtypes`:

1. Treat _int_ both as _np.int32_ and _np.int64_. If we have columns with both 64- and 32-bit integers we'll keep all of them
     I implemented this behavior in the current version, so now [this example](https://github.com/pandas-dev/pandas/issues/36596#issue-708024686) and [this exaple](https://github.com/pandas-dev/pandas/pull/36808#discussion_r498951543) should both work
2. Map _int_ to either _np.int32_ or _np.int64_ depending on the type of the given dataframe (_np.int32_, _np.int64_)
3. Raise a warning / error that _int_ is ambiguous, and either _np.int32_ or _np.int64_ should be provided

Another possible routes:
1. Change [maybe_convert_objects](https://github.com/pandas-dev/pandas/blob/d3788525ee368db0b6565b72ab4eeba9140fd1d4/pandas/_libs/lib.pyx#L2115) so it would follow [numpy's behavior](https://github.com/numpy/numpy/issues/9464)
2. Map_int_ to _np.int64_ all the time, as was done previously in this pull request @arw2019 I merged this branch to the latest master.
@jbrockmendel  I added a separate unit test.
@jreback As you [suggested](https://github.com/pandas-dev/pandas/pull/36808#discussion_r519529870), I moved code from [pandas_dtype](https://github.com/pandas-dev/pandas/blob/4b75067f4f5b01744a9565a21d5ac5057c6a7c43/pandas/core/dtypes/common.py#L1799) into [DataFrame.select_dtypes](https://github.com/pandas-dev/pandas/blob/9769b9dbab018bb192b8776d2db40402b27c388c/pandas/core/frame.py#L3626). In detail, this method will treat _int_ both as _np.int32_ and _np.int64_. Having columns with both 64- and 32-bit integers it will return all of them.
     I implemented this behavior in the current version, so now [this example](https://github.com/pandas-dev/pandas/issues/36596#issue-708024686) and [that exaple](https://github.com/pandas-dev/pandas/pull/36808#discussion_r498951543) should both work. @jreback I have updated this pull request according to your latest review. Could you take a look at it again? thanks @OlehKSS very nice!