agg behaviour depends on the number of arguments of the function
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd

df = pd.DataFrame({'a': [1,2,3,4,5,6],
 'b': [1,1,0,1,1,0],
 'c': ['x','x','x','z','z','z'],
 'd': ['s','s','s','d','d','d']})

def myprint2(x, y):
    print(x)

df.groupby(['c'])[['a', 'b']].agg(myprint2, 0)
```
#### Problem description

In the particular case of a groupby over 1 column ('c'), asking a subset of columns ('a' and 'b') and passing agg a function of 2 arguments ('mypring2'), a DataFrame of the full groups (including all columns) is passed to myprint2:
```python
>>>> df.groupby(['c'])[['a', 'b']].agg(myprint2, 0)
   a  b  c  d
0  1  1  x  s
1  2  1  x  s
2  3  0  x  s
   a  b  c  d
3  4  1  z  d
4  5  1  z  d
5  6  0  z  d
```
For every group, I would have expected 2 calls of myprint2, one with Series 'a' and one with Series 'b'. This is particularly confusing, because agg would have the expected behaviour if groupby is called over a list or if a function of 1 argument is passed to agg:

#### Expected Output
I would have expected the same as if groupby is called over a list or if a function of 1 argument is passed to agg:
```python
>>>> df.groupby(['c', 'd'])[['a', 'b']].agg(myprint2, 0)
0    1
1    2
2    3
Name: a, dtype: int64
3    4
4    5
5    6
Name: a, dtype: int64
0    1
1    1
2    0
Name: b, dtype: int64
3    1
4    1
5    0
Name: b, dtype: int64
>>>> df.groupby(['c'])[['a', 'b']].agg(print)
0    1
1    2
2    3
Name: a, dtype: int64
3    4
4    5
5    6
Name: a, dtype: int64
0    1
1    1
2    0
Name: b, dtype: int64
3    1
4    1
5    0
Name: b, dtype: int64
```


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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.4.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
machine          : AMD64
processor        : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : None.None

pandas           : 1.0.3
numpy            : 1.18.2
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 20.0.2
setuptools       : 46.1.1
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : None
IPython          : None
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : None
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : None
pyxlsb           : None
s3fs             : None
scipy            : 1.4.1
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : 1.2.0
xlwt             : None
xlsxwriter       : None
numba            : None


</details>
