BUG: highlight_min and highlight_max do not work with pd.NA
- [x] I have checked that this issue has not already been reported.

- [x] I have confirmed this bug exists on the latest version of pandas.

- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.

---


#### Code Sample, a copy-pastable example

```python
import pandas as pd
import numpy as np

df_ok = pd.DataFrame(dict(a=[np.nan, -1, 1], b=[np.nan, -1, 1]))
df_bug = pd.DataFrame(dict(a=[pd.NA, -1, 1], b=[pd.NA, -1, 1]))
```

#### Problem description

`df_ok.style.highlight_min()` works correctly while `df_bug.style.highlight_min()` returns the following error:

```
TypeError: boolean value of NA is ambiguous
```


#### Fix

For `highlight_min`, the error comes from https://github.com/pandas-dev/pandas/blob/v1.3.1/pandas/io/formats/style.py#L2367. Specifically, from the following line:
```
np.where(data == np.nanmin(data.to_numpy()), props, "")
```

I'm assuming this could be fixed by replacing it with:
```
np.where(data == data.min(skipna=True), props, "")
```

or by making sure ```to_numpy``` converts `pd.NA` to `np.nan`?

Happy to implement a fix.

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

<details>

INSTALLED VERSIONS
------------------
commit           : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python           : 3.8.3.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 20.4.0
Version          : Darwin Kernel Version 20.4.0: Thu Apr 22 21:46:47 PDT 2021; root:xnu-7195.101.2~1/RELEASE_X86_64
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : en_US.UTF-8
LANG             : en_GB.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.3.1
numpy            : 1.19.2
pytz             : 2021.1
dateutil         : 2.8.1
pip              : 20.0.2
setuptools       : 52.0.0.post20210125
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.3
IPython          : 7.20.0
pandas_datareader: None
bs4              : 4.9.3
bottleneck       : None
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : 3.4.2
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pyxlsb           : None
s3fs             : None
scipy            : 1.6.2
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : None

</details>
