BUG: .groupby.agg inconsistent passed object to udf with kwarg presence

[camriddell]

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• I have confirmed this bug exists on the latest version of pandas.

• I have confirmed this bug exists on the main branch of pandas.

Reproducible Example

import pandas as pd

df = pd.DataFrame({
    'group': [*('a' * 5), *('b' * 5)],
    'value1': range(10),
    'value2': range(11, 21),
})

def ufunc(s, irrelevant=None):
    print(type(s))
    print(s)

grouped = df.groupby('group')

print('--- no kwargs passed to ufunc')
grouped.agg(ufunc)

print()

print('--- passing kwarg to ufunc')
grouped.agg(ufunc, irrelevant=None)


Produces:
```python
--- no kwargs passed to ufunc
<class 'pandas.core.series.Series'>
0    0
1    1
2    2
3    3
4    4
Name: value1, dtype: int64
<class 'pandas.core.series.Series'>
5    5
6    6
7    7
8    8
9    9
Name: value1, dtype: int64
<class 'pandas.core.series.Series'>
0    11
1    12
2    13
3    14
4    15
Name: value2, dtype: int64
<class 'pandas.core.series.Series'>
5    16
6    17
7    18
8    19
9    20
Name: value2, dtype: int64

--- passing kwarg to ufunc
<class 'pandas.core.frame.DataFrame'>
  group  value1  value2
0     a       0      11
1     a       1      12
2     a       2      13
3     a       3      14
4     a       4      15
<class 'pandas.core.frame.DataFrame'>
  group  value1  value2
5     b       5      16
6     b       6      17
7     b       7      18
8     b       8      19
9     b       9      20

### Issue Description

I couldn't find an open issue that was the exact same as this. But it seems that `groupby(…).agg` with a user defined function seems to change the object passed into the function depending on the presence of keyword arguments.

In the example above, I am taking the same `Groupby` object and passing a udf into the `.agg` method. As highlighted by the documentation, I would expect each group to be passed as a `DataFrame`. However what is unexpected is that:

1. In the first sample output, we can see each grouped `Series` is into the UDF.
2. In the second sample output where I pass a keyword argument to the UDF, it seems that the entire `DataFrame` is passed into the udf.

### Expected Behavior

I would expect that the presence of keyword arguments would not change the input type of the passed objects to a `Groupby.agg`. Moreover, according to [DataFrame.GroupBy.aggregate](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.aggregate.html) udf's should operate on `DataFrame`s and not individual `Series`.

Expected output (created by swapping `.agg` for `.apply`):
```python
--- no kwargs passed to ufunc
<class 'pandas.core.frame.DataFrame'>
  group  value1  value2
0     a       0      11
1     a       1      12
2     a       2      13
3     a       3      14
4     a       4      15
<class 'pandas.core.frame.DataFrame'>
  group  value1  value2
5     b       5      16
6     b       6      17
7     b       7      18
8     b       8      19
9     b       9      20

--- passing kwarg to ufunc
<class 'pandas.core.frame.DataFrame'>
  group  value1  value2
0     a       0      11
1     a       1      12
2     a       2      13
3     a       3      14
4     a       4      15
<class 'pandas.core.frame.DataFrame'>
  group  value1  value2
5     b       5      16
6     b       6      17
7     b       7      18
8     b       8      19
9     b       9      20

Installed Versions

Version : #1 SMP Fri, 22 Jul 2022 23:29:06 +0000
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.4.3
numpy : 1.21.5
pytz : 2021.3
dateutil : 2.8.2
setuptools : 58.1.0
pip : 21.2.4
Cython : None
pytest : 7.0.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.8.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.3
IPython : 8.0.1
pandas_datareader: None
bs4 : 4.10.0
bottleneck : None
brotli : None
fastparquet : None
fsspec : 2022.5.0
gcsfs : None
markupsafe : 2.0.1
matplotlib : 3.5.1
numba : None
numexpr : None
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : 8.0.0
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.7.3
snappy : None
sqlalchemy : 1.4.32
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
zstandard : None

[camriddell]

Duplicate of #47701