BUG: Errors caused by DataFrame.all(..., skipna=False, ...) in rows without na values.
- [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.

---

I'm familiar with several issues being reported relating to skipna=False, but I have not seen this particular problem elsewhere. In short, the 'skipna=False' setting causes errors in rows not having any missing values.

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

```python
# Minimal code to reproduce in pandas 1.2.4:
import pandas as pd
df1 = pd.DataFrame({"A": [False,pd.NA,pd.NA], "B": [True,True,False]})
df1
       A      B
0  False   True
1   <NA>   True
2   <NA>  False

df1.all(axis=1, skipna=False)
0     True
1     True
2    False
dtype: bool

# And again with experimental BooleanDtype
df2 = pd.DataFrame({"A": [False,pd.NA,pd.NA], "B": [True,True,False]}, dtype=pd.BooleanDtype())
df2
       A      B
0  False   True
1   <NA>   True
2   <NA>  False

df2.all(axis=1, skipna=False)
0    True
1    True
2    True
dtype: bool
```

#### Problem description

With df1 row '0' is clearly wrong; with df2 '0' and '2' are undeniably wrong. The rest are imho also suspicious, but they at least follow the documented behavior of:
> If skipna is False, then NA are treated as True, because these are not equal to zero.

...even if I do believe the mathematically correct answers are as given e.g. here:
    https://www.ibm.com/docs/en/spss-statistics/SaaS?topic=command-missing-values-logical-operators-if
In other words, row '1' should, imho, evaluate to 'NA'.

#### Expected Output

Rows '0' and '2' should clearly evaluate to 'False'.
Row '1' should maybe evaluate to 'NA'.

<details>

INSTALLED VERSIONS
------------------
commit           : 2cb96529396d93b46abab7bbc73a208e708c642e
python           : 3.9.2.final.0
python-bits      : 64
OS               : Linux
OS-release       : 3.10.0-957.12.2.el7.x86_64
Version          : #1 SMP Fri Apr 19 21:09:07 UTC 2019
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : C
LANG             : C
LOCALE           : None.None

pandas           : 1.2.4
numpy            : 1.20.1
pytz             : 2021.1
dateutil         : 2.8.1
pip              : 21.0.1
setuptools       : 49.6.0.post20210108
Cython           : None
pytest           : 6.2.2
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.3
IPython          : None
pandas_datareader: None
bs4              : 4.9.3
bottleneck       : None
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : None
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pyxlsb           : None
s3fs             : None
scipy            : None
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : None

</details>

