BUG: groupby dropna=False with nan value in groupby causes ValueError when apply() 
- [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, version `1.1.1`

- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.

---

#### Code Sample, a copy-pastable example

```python
import pandas as pd
import numpy as np

df = pd.DataFrame({
    'groups': ['a', 'a', 'b', np.nan],
    'tonnages': [10, 10, 20, 30]
})
dfg = df.groupby('groups', dropna=False)
rv = dfg.apply(lambda grp: pd.DataFrame({'values': list(range(len(grp)))}))
```

#### Problem description

`ValueError` raised on `dfg.apply(lambda grp: ...)`; with the following stacktrace:
<details>

```python
../../env3.8/lib/python3.8/site-packages/pandas/core/groupby/groupby.py:859: in apply
    result = self._python_apply_general(f, self._selected_obj)
../../env3.8/lib/python3.8/site-packages/pandas/core/groupby/groupby.py:894: in _python_apply_general
    return self._wrap_applied_output(
../../env3.8/lib/python3.8/site-packages/pandas/core/groupby/generic.py:1230: in _wrap_applied_output
    return self._concat_objects(keys, values, not_indexed_same=not_indexed_same)
../../env3.8/lib/python3.8/site-packages/pandas/core/groupby/groupby.py:1145: in _concat_objects
    result = concat(
../../env3.8/lib/python3.8/site-packages/pandas/core/reshape/concat.py:274: in concat
    op = _Concatenator(
../../env3.8/lib/python3.8/site-packages/pandas/core/reshape/concat.py:454: in __init__
    self.new_axes = self._get_new_axes()
../../env3.8/lib/python3.8/site-packages/pandas/core/reshape/concat.py:519: in _get_new_axes
    return [
../../env3.8/lib/python3.8/site-packages/pandas/core/reshape/concat.py:520: in <listcomp>
    self._get_concat_axis() if i == self.bm_axis else self._get_comb_axis(i)
../../env3.8/lib/python3.8/site-packages/pandas/core/reshape/concat.py:576: in _get_concat_axis
    concat_axis = _make_concat_multiindex(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

indexes = [RangeIndex(start=0, stop=2, step=1), RangeIndex(start=0, stop=1, step=1), RangeIndex(start=0, stop=1, step=1)]
keys = Index(['a', 'b', nan], dtype='object', name='groups')
levels = [Index(['a', 'b', nan], dtype='object', name='groups')]
names = ['groups']

    def _make_concat_multiindex(indexes, keys, levels=None, names=None) -> MultiIndex:
    
        if (levels is None and isinstance(keys[0], tuple)) or (
            levels is not None and len(levels) > 1
        ):
            zipped = list(zip(*keys))
            if names is None:
                names = [None] * len(zipped)
    
            if levels is None:
                _, levels = factorize_from_iterables(zipped)
            else:
                levels = [ensure_index(x) for x in levels]
        else:
            zipped = [keys]
            if names is None:
                names = [None]
    
            if levels is None:
                levels = [ensure_index(keys)]
            else:
                levels = [ensure_index(x) for x in levels]
    
        if not all_indexes_same(indexes):
            codes_list = []
    
            # things are potentially different sizes, so compute the exact codes
            # for each level and pass those to MultiIndex.from_arrays
    
            for hlevel, level in zip(zipped, levels):
                to_concat = []
                for key, index in zip(hlevel, indexes):
                    mask = level == key
                    if not mask.any():
>                       raise ValueError(f"Key {key} not in level {level}")
E                       ValueError: Key nan not in level Index(['a', 'b', nan], dtype='object', name='groups')

../../env3.8/lib/python3.8/site-packages/pandas/core/reshape/concat.py:631: ValueError
```

</details>

#### Expected Output

No error should be raised. With the above, if i omit the `nan`:

```python
df = pd.DataFrame({
    'groups': ['a', 'a', 'b'],
    'tonnages': [10, 10, 20]
})
dfg = df.groupby('groups', dropna=False)
rv = dfg.apply(lambda grp: pd.DataFrame({'values': list(range(len(grp)))}))
```
Then it works successfully with `rv` being:

```python
          values
groups          
a      0       0
       1       1
b      0       0
```


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

<details>

INSTALLED VERSIONS
------------------
commit           : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python           : 3.8.2.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 18.7.0
Version          : Darwin Kernel Version 18.7.0: Thu Jun 18 20:50:10 PDT 2020; root:xnu-4903.278.43~1/RELEASE_X86_64
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_AU.UTF-8
LOCALE           : en_AU.UTF-8

pandas           : 1.1.1
numpy            : 1.18.4
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 20.1.1
setuptools       : 46.4.0
Cython           : 0.29.17
pytest           : 5.1.1
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : 0.9.6
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : 2.8.3 (dt dec pq3 ext lo64)
jinja2           : 2.11.2
IPython          : None
pandas_datareader: None
bs4              : None
bottleneck       : 1.3.1
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : None
numexpr          : 2.7.1
odfpy            : None
openpyxl         : 1.8.6
pandas_gbq       : None
pyarrow          : None
pytables         : None
pyxlsb           : None
s3fs             : None
scipy            : 1.4.1
sqlalchemy       : 1.3.12
tables           : 3.6.1
tabulate         : None
xarray           : None
xlrd             : 1.2.0
xlwt             : None
numba            : None

</details>
