Quantile function fails when performing groupby on Time Zone Aware Timestamps
#### Code Sample, a copy-pastable example if possible

Maybe **not a high priority bug**, but I have the feeling it can easily fixed. I just have not enough understanding on how it should be fixed. Please find below the MCVE to reproduce it:

```
import numpy as np
import pandas as pd

# Sample Dataset:
n = 200
c = np.random.choice([0,1,2], size=(n,))
d = np.random.randn(n)
t = pd.date_range(start='2020-04-19 00:00:00', freq='1T', periods=n, tz='UTC')
df = pd.DataFrame([r for r in zip(c, t, d)], columns=['category', 'timestamp', 'value'])
df['rtime'] = df['timestamp'].dt.floor('1H')

# Failing operation:
df.groupby('rtime').quantile([0.1, 0.5, 0.9])
```
#### Problem description

The traceback of the error is a bit laconic and I have not enough experience in Pandas source code to cover all details of this error:

```bash
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-35-aff2c9a206a6> in <module>
----> 1 df.groupby('rtime').quantile([0.1,0.2])

/usr/local/lib/python3.6/dist-packages/pandas/core/groupby/groupby.py in quantile(self, q, interpolation)
   1926                     interpolation=interpolation,
   1927                 )
-> 1928                 for qi in q
   1929             ]
   1930             result = concat(results, axis=0, keys=q)

/usr/local/lib/python3.6/dist-packages/pandas/core/groupby/groupby.py in <listcomp>(.0)
   1926                     interpolation=interpolation,
   1927                 )
-> 1928                 for qi in q
   1929             ]
   1930             result = concat(results, axis=0, keys=q)

/usr/local/lib/python3.6/dist-packages/pandas/core/groupby/groupby.py in _get_cythonized_result(self, how, cython_dtype, aggregate, needs_values, needs_mask, needs_ngroups, result_is_index, pre_processing, post_processing, **kwargs)
   2289                 func = partial(func, ngroups)
   2290 
-> 2291             func(**kwargs)  # Call func to modify indexer values in place
   2292 
   2293             if result_is_index:

pandas/_libs/groupby.pyx in pandas._libs.groupby.__pyx_fused_cpdef()

TypeError: No matching signature found
```
I have found similar issues on GitHub with the same exception, but I think it is too generic to be the same related problem. Additionally, I may have found a simple corner case issue with TZ aware timestamp.

I had some hard time to reproduce the error when building the MCVE, finally I found out that it is related to the existence of an extra columns holding Time Zone aware timestamps.

**Maybe the fix it is just about updating function signature to add TZ aware timestamps.**

The problem can be circonvolved using one of the following writing:

```python
df.groupby('rtime')['value'].quantile([0.1,0.2])
```
Or:
```python
df['timestamp'] = df['timestamp'].dt.tz_convert(None)
df.groupby('rtime').quantile([0.1,0.2])
```

Or:
```python
df.pop('timestamp')
df.groupby('rtime').quantile([0.1,0.2])
```

Which strongly suggests it is the existence of the TZ Aware extra column `timestamp` that makes the function `quantile` fail.

#### Expected Output

Expected output might be no distinction in flow when performing `groupby` operations on dataframe holding TimeZone aware timestamp as it does with TZ naive timestamp.

Note: Thank you for building such a great tool, `pandas` is a first class middleware. Your efforts are strongly appreciated. Let me know how I can help, I would be happy to understand how this can be corrected.

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

<details>
commit           : None
python           : 3.6.9.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.15.0-91-generic
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : C.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.0.3
numpy            : 1.18.2
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 9.0.1
setuptools       : 46.1.3
Cython           : 0.29.14
pytest           : 5.3.2
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : 1.1.8
lxml.etree       : 4.3.4
html5lib         : 0.999999999
pymysql          : None
psycopg2         : 2.8.4 (dt dec pq3 ext lo64)
jinja2           : 2.11.1
IPython          : 7.13.0
pandas_datareader: None
bs4              : 4.7.1
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.3.4
matplotlib       : 3.2.1
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 0.13.0
pytables         : None
pytest           : 5.3.2
pyxlsb           : None
s3fs             : None
scipy            : 1.4.1
sqlalchemy       : 1.3.15
tables           : None
tabulate         : 0.8.3
xarray           : None
xlrd             : 1.2.0
xlwt             : 1.3.0
xlsxwriter       : 1.1.8
numba            : None
</details>
