pandas groupby sum min_count misbehaves
#### Code Sample, a copy-pastable example if possible

```python
d=pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
d['col2']=d['col2'].astype(str)
d['col1']=np.nan
d=d.groupby(lambda x:x, axis=1).sum(min_count=1)
```
#### Problem description

My hope is that, by using min_count=1, pandas will return NaN when all columns being summed up are NaN. However, now it is returning 0 instead of NaN. Any idea why?

#### Actual output: 

 ```
  col1  col2
0   0.0   3.0
1   0.0   4.0
```

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#### Expected Output

```
   col1  col2
0   Nan   3.0
1   Nan   4.0
```

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

<details>

[paste the output of ``pd.show_versions()`` here below this line]

INSTALLED VERSIONS
------------------
commit: None
python: 2.7.15.final.0
python-bits: 64
OS: Darwin
OS-release: 18.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.23.4
pytest: 3.8.2
pip: 10.0.1
setuptools: 39.1.0
Cython: None
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 5.8.0
sphinx: None
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: 1.2.1
tables: None
numexpr: None
feather: None
matplotlib: 2.2.3
openpyxl: None
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

</details>
