DataFrameGroupBy.quantile raises for non-numeric dtypes rather than dropping columns
In pandas 0.24.x, we had

```python
In [1]: import pandas as pd

In [2]: pd.DataFrame({"A": ['a', 'b']}).groupby([0, 0]).quantile()
Out[2]:
Empty DataFrame
Columns: []
Index: []
``` 

In 0.25.0, we have

```python
In [3]: pd.DataFrame({"A": ['a', 'b']}).groupby([0, 0]).quantile()
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-3-8152ffc0932b> in <module>
----> 1 pd.DataFrame({"A": ['a', 'b']}).groupby([0, 0]).quantile()

~/sandbox/pandas/pandas/core/groupby/groupby.py in quantile(self, q, interpolation)
   1908             post_processing=post_processor,
   1909             q=q,
-> 1910             interpolation=interpolation,
   1911         )
   1912

~/sandbox/pandas/pandas/core/groupby/groupby.py in _get_cythonized_result(self, how, grouper, aggregate, cython_dtype, needs_values, needs_mask, needs_ngroups, result_is_index, pre_processing, post_processing, **kwargs)
   2236                 vals = obj.values
   2237                 if pre_processing:
-> 2238                     vals, inferences = pre_processing(vals)
   2239                 func = partial(func, vals)
   2240

~/sandbox/pandas/pandas/core/groupby/groupby.py in pre_processor(vals)
   1875             if is_object_dtype(vals):
   1876                 raise TypeError(
-> 1877                     "'quantile' cannot be performed against 'object' dtypes!"
   1878                 )
   1879

TypeError: 'quantile' cannot be performed against 'object' dtypes!
```

This is most relevant for mixed dataframes

```python
In [6]: df = pd.DataFrame({"A": [0, 1], 'B': ['a', 'b']})

In [7]: df.groupby([0, 1]).quantile(0.5)

...

TypeError: 'quantile' cannot be performed against 'object' dtypes!

```