BUG: concat behaves differently for bool/boolean dtypes than it does for int64/Int64
- [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

int1 = pd.Series([1, 2], dtype='int64')
int2 = pd.Series([pd.NA, 4], dtype='Int64')

pd.concat([int1, int2])
```

```
0       1
1       2
0    <NA>
1       4
dtype: Int64
```

```python
bool1 = pd.Series([True, False], dtype='bool')
bool2 = pd.Series([pd.NA, True], dtype='boolean')

pd.concat([bool1, bool2])
```

```
0     True
1    False
0     <NA>
1     True
dtype: object
```

#### Problem description

When running `concat` with two series, one of `int64` dtype and one of `Int64` dtype containing `pd.NA`, the resulting series has a dtype of `Int64`, which makes sense as this type is able to contain data from both. However, performing this same test on series with types of `bool` and `boolean`, results in a series with a dtype of `object`. It seems for consistency with the other behavior the series that results from concat should be `boolean` and not `object`.

#### Expected Output
The series resulting from running concat with a `bool` series and a `boolean` series, should be a series with a `boolean` dtype instead of `object`.
```
0     True
1    False
0     <NA>
1     True
dtype: boolean
```

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

<details>

INSTALLED VERSIONS
------------------
commit           : 2cb96529396d93b46abab7bbc73a208e708c642e
python           : 3.7.4.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 18.7.0
Version          : Darwin Kernel Version 18.7.0: Tue Jan 12 22:04:47 PST 2021; root:xnu-4903.278.56~1/RELEASE_X86_64
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.2.4
numpy            : 1.19.5
pytz             : 2020.5
dateutil         : 2.8.1
pip              : 20.3.3
setuptools       : 40.8.0
Cython           : None
pytest           : 5.2.0
hypothesis       : None
sphinx           : 3.2.1
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.2
IPython          : 7.18.1
pandas_datareader: None
bs4              : None
bottleneck       : None
fsspec           : 0.8.5
fastparquet      : 0.5.0
gcsfs            : None
matplotlib       : 3.2.2
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 2.0.0
pyxlsb           : None
s3fs             : None
scipy            : 1.6.0
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : 0.52.0

</details>
