Filtering dataframe with sparse column leads to NAs in sparse column
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd

df1 = pd.DataFrame({"A": pd.SparseArray([0, 0, 0]), 'B': [1,2,3]})
# df1_filtered will have NAs in column A
df1_filtered = df1.loc[df1['B'] != 2]

df2 = pd.DataFrame({"A": pd.SparseArray([0, 1, 0]), 'B': [1,2,3]})
# df2_filtered has no NAs in column A
df2_filtered = df2.loc[df2['B'] != 2]
```
where `df1_filtered` will look like
```
	A	B
0	NaN	1
2	NaN	3
```
and `df2_filtered` like
```
	A	B
0	0	1
2	0	3
```
#### Problem description

Filtering a dataframe with an all-zero sparse column can lead to NAs in the sparse column.

#### Expected Output

Both data frames should be the same, as filtering a dataframe with non-missing data should not lead to missing data.

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

<details>

[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit           : None

pandas           : 0.25.0
numpy            : 1.16.2
pytz             : 2019.1
dateutil         : 2.8.0
pip              : 19.2.1
setuptools       : 39.1.0
Cython           : None
pytest           : 4.3.1
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : 2.8.3 (dt dec pq3 ext lo64)
jinja2           : 2.10.1
IPython          : 7.6.1
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : 0.2.1
lxml.etree       : None
matplotlib       : 3.1.1
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 0.13.0
pytables         : None
s3fs             : 0.2.0
scipy            : 1.2.1
sqlalchemy       : 1.3.5
tables           : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None
</details>
