BUG: pd.NA is not compatible with searchsorted
#### Code Sample, a copy-pastable example if possible
On `master` trying to use `pd.NA` as an input to `searchsorted` fails, and trying to use the `searchsorted` of an array containing `pd.NA` also fails:

```python-traceback
In [1]: import numpy as np; import pandas as pd; pd.__version__
Out[1]: '1.0.0rc0+15.g4e2546d89'

In [2]: s = pd.Series([-1, 1, 3, 5])

In [3]: arr_pd_na = pd.array([0, 1, 2, pd.NA])

In [4]: s.searchsorted(arr_pd_na)
---------------------------------------------------------------------------
TypeError: boolean value of NA is ambiguous

In [5]: s.searchsorted(pd.NA)
---------------------------------------------------------------------------
TypeError: boolean value of NA is ambiguous

In [6]: arr_pd_na.searchsorted(10)
---------------------------------------------------------------------------
TypeError: boolean value of NA is ambiguous
```

Note that the `np.nan` equivalent works fine:
```python-traceback
In [7]: arr_np_nan = np.array([0, 1, 2, np.nan])

In [8]: s.searchsorted(arr_np_nan)
Out[8]: array([1, 1, 2, 4])

In [9]: s.searchsorted(np.nan)
Out[9]: 4

In [10]: arr_np_nan.searchsorted(10)
Out[10]: 3
```

This has downstream effects on anything that relies on `searchsorted`, e.g. `pd.cut`, which has the same failing behavior as above for `pd.NA` but succeeds for `np.nan`:
```python-traceback
In [11]: pd.cut(arr_pd_na, bins=3)
---------------------------------------------------------------------------
TypeError: boolean value of NA is ambiguous

In [12]: pd.cut(arr_np_nan, bins=3)
Out[12]: 
[(-0.002, 0.667], (0.667, 1.333], (1.333, 2.0], NaN]
Categories (3, interval[float64]): [(-0.002, 0.667] < (0.667, 1.333] < (1.333, 2.0]]
```

#### Problem description
`pd.NA` is not compatible with `searchsorted`.

#### Expected Output

I'd expect the output for the `pd.NA` operations above to match the output of the equivalent `np.nan` operations.

#### Output of ``pd.show_versions()``

<details>


INSTALLED VERSIONS
------------------
commit           : 4e2546d89260fda592332a3988573c26edc7152c
python           : 3.7.4.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.19.14-041914-generic
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.0.0rc0+15.g4e2546d89
numpy            : 1.17.2
pytz             : 2019.2
dateutil         : 2.8.0
pip              : 19.2.3
setuptools       : 41.6.0.post20191030
Cython           : 0.29.13
pytest           : 5.2.0
hypothesis       : 4.36.2
sphinx           : 1.8.5
blosc            : None
feather          : None
xlsxwriter       : 1.2.1
lxml.etree       : 4.4.1
html5lib         : 1.0.1
pymysql          : None
psycopg2         : None
jinja2           : 2.10.1
IPython          : 7.8.0
pandas_datareader: None
bs4              : 4.8.0
bottleneck       : 1.2.1
fastparquet      : 0.3.2
gcsfs            : None
lxml.etree       : 4.4.1
matplotlib       : 3.1.1
numexpr          : 2.7.0
odfpy            : None
openpyxl         : 3.0.0
pandas_gbq       : None
pyarrow          : 0.15.0
pytables         : None
pytest           : 5.2.0
s3fs             : 0.3.4
scipy            : 1.3.1
sqlalchemy       : 1.3.8
tables           : 3.5.1
tabulate         : None
xarray           : 0.13.0
xlrd             : 1.2.0
xlwt             : 1.3.0
xlsxwriter       : 1.2.1
numba            : 0.46.0


</details>
