BUG: com.maybe_make_list does not resolve correctly for np.array
### 

- [X] I have checked that this issue has not already been reported.

- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.

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


### Reproducible Example

```python
from pandas.core.common import maybe_make_list
import numpy as np

arr = np.array(["A", "C"])
result = maybe_make_list(arr)
print(result)

[array(['A', 'C'], dtype='<U1')]
```


### Issue Description

This discussion started in [this](https://github.com/pandas-dev/pandas/pull/41022/files#r730277602) thread. Should `com.maybe_make_list` take `np.array` into account and maybe some other list like objects as well. For example Index objects.

### Expected Behavior
```python
from pandas.core.common import maybe_make_list
import numpy as np

arr = np.array(["A", "C"])
result = maybe_make_list(arr)
print(result)

array(['A', 'C'], dtype='<U1')
```
### Installed Versions

<details>

INSTALLED VERSIONS
------------------
commit           : 445bb9f57c3d59141a059a5afd2762aef2e96cad
python           : 3.8.12.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 20.6.0
Version          : Darwin Kernel Version 20.6.0: Mon Aug 30 06:12:21 PDT 2021; root:xnu-7195.141.6~3/RELEASE_X86_64
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : None.UTF-8

pandas           : 1.4.0.dev0+904.g445bb9f57c.dirty
numpy            : 1.21.2
pytz             : 2021.3
dateutil         : 2.8.2
pip              : 21.2.4
setuptools       : 58.0.4
Cython           : 0.29.24
pytest           : 6.2.5
hypothesis       : 6.23.2
sphinx           : 4.2.0
blosc            : 1.10.6
feather          : None
xlsxwriter       : 3.0.1
lxml.etree       : 4.6.3
html5lib         : 1.1
pymysql          : None
psycopg2         : None
jinja2           : 3.0.2
IPython          : 7.28.0
pandas_datareader: None
bs4              : 4.10.0
bottleneck       : 1.3.2
fsspec           : 2021.10.0
fastparquet      : 0.7.1
gcsfs            : 2021.10.0
matplotlib       : 3.4.3
numexpr          : 2.7.3
odfpy            : None
openpyxl         : 3.0.9
pandas_gbq       : None
pyarrow          : 5.0.0
pyxlsb           : None
s3fs             : 2021.10.0
scipy            : 1.7.1
sqlalchemy       : 1.4.25
tables           : 3.6.1
tabulate         : 0.8.9
xarray           : 0.18.2
xlrd             : 2.0.1
xlwt             : 1.3.0
numba            : None


</details>
