API: Series[bool][key] = np.nan -> cast to object
- [x] closes #38692
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry

sits on top of #38688
yikes, the existing behavior is even worse than i thought:

```
ser = pd.Series([True, False])
ser2 = ser.copy()

ser.iloc[1] = np.nan

ser2.iloc[1] = np.float64("NAN")

>>> ser
0    1.0
1    NaN
dtype: float64

>>> ser2
0    True
1     NaN
dtype: object
```

This fixes that; ill add a test.  gentle ping @jreback this is probably the toughest of the outstanding setitem-related PRs > have a sub-section in api breaking.

added + green thanks, very nice way cool.  between this and #39163 ill be un-blocked on unifying a bunch of casting behavior i think The appears to be a performance regression here  https://pandas.pydata.org/speed/pandas/#frame_methods.MaskBool.time_frame_mask_bools

There has been some improvement since.

```
(pandas-dev) simon@T3630:~/pandas/asv_bench (master)$ asv compare v1.2.5 1.3.x |grep time_frame_mask_bools
+     5.62±0.09ms       30.8±0.5ms     5.49  frame_methods.MaskBool.time_frame_mask_bools
```

profile

<details>

```
(pandas-dev) simon@T3630:~/pandas/asv_bench (master)$ asv profile frame_methods.MaskBool.time_frame_mask_bools 3217b436c754bdf62081408f0417ff2061702e75|head -n 30
· Discovering benchmarks
·· Uninstalling from conda-py3.8-Cython0.29.21-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
·· Installing 3217b436 <v1.3.0rc0~1332> into conda-py3.8-Cython0.29.21-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
· Profile data does not already exist. Running profiler now.
·· Benchmarking conda-py3.8-Cython0.29.21-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
··· Running (frame_methods.MaskBool.time_frame_mask_bools--).
··· frame_methods.MaskBool.time_frame_mask_bools            42.4±0.5ms

Tue Jun 29 14:36:40 2021    /tmp/tmpoghq5rs1

         41910 function calls (41895 primitive calls) in 0.051 seconds

   Ordered by: cumulative time

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        1    0.000    0.000    0.051    0.051 {built-in method builtins.exec}
        1    0.000    0.000    0.051    0.051 /home/simon/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py:540(method_caller)
        1    0.001    0.001    0.051    0.051 /home/simon/pandas/asv_bench/benchmarks/frame_methods.py:307(time_frame_mask_bools)
        1    0.000    0.000    0.051    0.051 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/generic.py:9116(mask)
        1    0.000    0.000    0.051    0.051 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/generic.py:8963(where)
        1    0.000    0.000    0.051    0.051 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/generic.py:8801(_where)
        5    0.000    0.000    0.049    0.010 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/internals/managers.py:380(apply)
        1    0.000    0.000    0.048    0.048 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/internals/managers.py:548(where)
      2/1    0.000    0.000    0.047    0.047 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/internals/blocks.py:1259(where)
        1    0.000    0.000    0.037    0.037 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/internals/blocks.py:544(_maybe_downcast)
        1    0.000    0.000    0.037    0.037 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/internals/blocks.py:551(<listcomp>)
        1    0.000    0.000    0.037    0.037 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/internals/blocks.py:553(downcast)
        1    0.001    0.001    0.037    0.037 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/internals/blocks.py:474(split_and_operate)
      500    0.000    0.000    0.021    0.000 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/internals/blocks.py:583(f)
      500    0.001    0.000    0.021    0.000 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/dtypes/cast.py:192(maybe_downcast_to_dtype)
```

</details>

commit before

<details>

```
(pandas-dev) simon@T3630:~/pandas/asv_bench (master)$ asv profile frame_methods.MaskBool.time_frame_mask_bools 3217b436c754bdf62081408f0417ff2061702e75~1|head -n 30
· Discovering benchmarks
·· Uninstalling from conda-py3.8-Cython0.29.21-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
·· Installing 6e579ed3 <v1.3.0rc0~1333> into conda-py3.8-Cython0.29.21-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
· Profile data does not already exist. Running profiler now.
·· Benchmarking conda-py3.8-Cython0.29.21-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
··· Running (frame_methods.MaskBool.time_frame_mask_bools--).
··· frame_methods.MaskBool.time_frame_mask_bools           5.29±0.03ms

Tue Jun 29 14:37:42 2021    /tmp/tmpqkntmn4t

         10274 function calls (10261 primitive calls) in 0.007 seconds

   Ordered by: cumulative time

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        1    0.000    0.000    0.007    0.007 {built-in method builtins.exec}
        1    0.000    0.000    0.007    0.007 /home/simon/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py:540(method_caller)
        1    0.000    0.000    0.007    0.007 /home/simon/pandas/asv_bench/benchmarks/frame_methods.py:307(time_frame_mask_bools)
        1    0.000    0.000    0.007    0.007 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/generic.py:9116(mask)
        1    0.000    0.000    0.006    0.006 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/generic.py:8963(where)
        1    0.000    0.000    0.006    0.006 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/generic.py:8801(_where)
        5    0.000    0.000    0.005    0.001 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/internals/managers.py:380(apply)
        1    0.000    0.000    0.004    0.004 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/internals/managers.py:548(where)
        1    0.000    0.000    0.004    0.004 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/internals/blocks.py:1277(where)
      501    0.000    0.000    0.001    0.000 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/dtypes/common.py:1354(is_bool_dtype)
        1    0.001    0.001    0.001    0.001 {method 'take' of 'numpy.ndarray' objects}
        1    0.000    0.000    0.001    0.001 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/computation/expressions.py:239(where)
        1    0.000    0.000    0.001    0.001 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/computation/expressions.py:165(_where_numexpr)
        1    0.001    0.001    0.001    0.001 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/numexpr/necompiler.py:767(evaluate)
        1    0.000    0.000    0.001    0.001 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/internals/blocks.py:1351(<listcomp>)
```

</details>

I guess this is expected?
 > I guess this is expected?

Can't comment on the size of the perf hit, but the existence is unsurprising since we're casting to object