BUG: DataFrame aggregation methods ignore numeric_only= when level= is specified
- [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

[notebook](https://colab.research.google.com/drive/1NYuEzyU3QDERoY7KOvD_-HxfGBvG517F?usp=sharing)

```python
In [1]: import pandas as pd                                                                                         
   ...: df = pd.DataFrame({                                                                                         
   ...:     'int': [1,2,3],                               
   ...:     'flt': [3.14,1.0,2.0],                        
   ...:     'str': ['z', 'a', 'b'],                       
   ...:     'bool': [True, False, True],                  
   ...: }, index=['a','a','b'])                           
   ...: df                                                
Out[1]:                                                   
   int   flt str   bool                                   
a    1  3.14   z   True                                   
a    2  1.00   a  False                                   
b    3  2.00   b   True                                   
                                                          
In [2]: df.dtypes                                         
Out[2]:                                                   
int       int64                                           
flt     float64                                           
str      object                                           
bool       bool                                           
dtype: object                                             

In [3]: df.sum(numeric_only=True) # WAI without level= arg
Out[3]: 
int     6.00
flt     6.14
bool    2.00
dtype: float64

In [4]: df.sum()
Out[4]: 
int        6
flt     6.14
str      zab
bool       2
dtype: object

In [5]: df.sum(level=0, numeric_only=None) # behaves as if numeric_only is True no matter the input
Out[5]: 
   int   flt  bool
a    3  4.14     1
b    3  2.00     1

In [6]: df.sum(level=0, numeric_only=True)
Out[6]: 
   int   flt  bool
a    3  4.14     1
b    3  2.00     1

In [8]: df.max(level=0, numeric_only=None) # behaves as if numeric_only is None no matter the input
Out[8]: 
   int   flt str  bool
a    2  3.14   z  True
b    3  2.00   b  True

In [9]: df.max(level=0, numeric_only=True)
Out[9]: 
   int   flt str  bool
a    2  3.14   z  True
b    3  2.00   b  True
```

#### Problem description
The documentation for numeric aggregation methods on DataFrame (mean, median, sum, max, ..) indicates that `numeric_only` will drop all but float, int, and boolean columns. This works as expected in the traditional mode, but the argument seems to be ignored when doing a grouped aggregation via the `level=` arg. Some methods (e.g. sum) behave as if numeric_only is always True, some (e.g. max) behave as if its always None.

#### Expected Output
I'd expect that `str` would be dropped iff `numeric_only=True`, all other examples would include the `str` column. 

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

<details>
INSTALLED VERSIONS     
------------------     
commit           : f2c8480af2f25efdbd803218b9d87980f416563e
python           : 3.8.6.final.0
python-bits      : 64  
OS               : Linux
OS-release       : 5.7.17-1rodete5-amd64
Version          : #1 SMP Debian 5.7.17-1rodete5 (2021-01-08)
machine          : x86_64
processor        : 
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.2.3
numpy            : 1.19.5
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 20.2.1
setuptools       : 49.2.1
Cython           : 0.29.13
pytest           : 4.6.11
hypothesis       : None
sphinx           : 1.8.5
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : 2.8.6 (dt dec pq3 ext lo64)
jinja2           : 2.11.2
IPython          : 7.19.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : None
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 3.0.0
pyxlsb           : None
s3fs             : None
scipy            : 1.4.1
sqlalchemy       : 1.3.20
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : None

</details>
