Pandas wrongly read csr_matrix with explicit 0
#### Code Sample, a copy-pastable example if possible

```python
>>> a = csr_matrix([[1, 0, 0], [2, 2, 0], [0, 2, 2]])
>>> a[0, 2] = 0
>>> a.todense()
matrix([[1, 0, 0],
        [2, 2, 0],
        [0, 2, 2]], dtype=int64)

>>> pd.DataFrame.sparse.from_spmatrix(a)
   0  1  2
0  1  0  0
1  2  2  0
2  0  2  0
```
Observe the difference in the bottom left value in the second matrix.

#### Problem description

When the sparse matrix (in our case the matrix with the explicit zero) is imported in pandas with `from_spmatrix` function at least one value that should not be zero becomes a zero. See the example below. Both matrices should be the same.

#### Expected Output

When converting sparse matrix to Pandas DataFrame values of the sparse array should stay the same. 

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

<details>

INSTALLED VERSIONS
------------------
commit           : None
pandas           : 0.25.1
numpy            : 1.16.5
pytz             : 2019.2
dateutil         : 2.8.0
pip              : 19.2.3
setuptools       : 40.8.0
Cython           : None
pytest           : 5.2.0
hypothesis       : None
sphinx           : 2.2.0
blosc            : None
feather          : None
xlsxwriter       : 1.2.1
lxml.etree       : 4.4.1
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.10.1
IPython          : 7.8.0
pandas_datareader: None
bs4              : 4.8.0
bottleneck       : 1.2.1
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.4.1
matplotlib       : 3.1.1
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : 1.3.1
sqlalchemy       : None
tables           : None
xarray           : None
xlrd             : 1.2.0
xlwt             : None
xlsxwriter       : 1.2.1

</details>
