Reindexing a sparse data structure with a different index results in losing the dtype
#### Code Sample, a copy-pastable example if possible

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

for dtype in (np.float32, np.int32, np.bool):
    s = pd.SparseSeries([1, 0], dtype=dtype)
    print('original:', s.dtype)
    print('reindexed:', s.reindex([0, 1, 2]).dtype)
```

**Output**
```
original: Sparse[float32, nan]
reindexed: Sparse[float64, nan]
original: Sparse[int32, 0]
reindexed: Sparse[float64, 0]
original: Sparse[bool, False]
reindexed: Sparse[float64, False]
```

#### Problem description

The output sparse series is always of type ```Sparse[float64]``` instead of the dtype passed in.

This looks to be a regression from v0.23.4. Perhaps it has to do with the new SparseArray rework in v0.24.x.

#### Expected Output

Ideally the dtype would not be lost. Although that wasn't exactly true in v0.23.4, the sparse float dtypes were not upcasted. My use case for casting to a sparse dtype is to save space, so the conversion to float64 breaks things.

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.13.0-16-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.24.2
pytest: 4.3.1
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.16.2
scipy: 1.2.1
pyarrow: 0.12.0
xarray: None
IPython: 7.3.0
sphinx: 1.8.5
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 2.2.3
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 4.3.2
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.1
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None

</details>
