[BUG]: Rolling.sum() calculated wrong values when axis is one and dtypes are mixed
- [x] closes #20649
- [x] closes #35596
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry

In case of ``axis=1`` and mixed dtypes, the ``_apply_blockwise`` did not calculate the sum for a complete row. Through converting the obj and consolidating the blocks we can avoid this.
Does this also potentially close https://github.com/pandas-dev/pandas/issues/35596? Thanks, I missed that issue. When the output you posted is the correct one, then yes, this fixes the issue. I will add a test. @jreback so we should raise if we get dtypes except float and int? I think we should just drop columns that are not coercible to float like `axis=0` behavior

```
In [1]: df = pd.DataFrame({'a': range(5), 'b': ['a'] * 5})

In [2]: df
Out[2]:
   a  b
0  0  a
1  1  a
2  2  a
3  3  a
4  4  a

In [3]: df.rolling(2).sum()
Out[3]:
     a
0  NaN
1  1.0
2  3.0
3  5.0
4  7.0
``` Thanks @mroeschke. Implemented it. lgtm ping on green. @jreback green  thanks @phofl very nice!