pd.concat with mixed sparse and dense data, kill structure



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

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

fill_value = 0 
#fill_value = np.nan

df0 = pd.DataFrame({'A': [1, fill_value, fill_value]})
df1 = pd.DataFrame({'B': ['a','b','c']})

c = pd.concat([df0, df1], axis=1)
print(c) # >>> OK
print(c['A']) # >>> OK
print(c['B']) # >>> OK

#========================
print("=====================")
df0_s = df0.to_sparse(fill_value = fill_value)
c = pd.concat([df0_s, df1], axis=1)
print(c) # >>> OK
print(c['A']) # >>> OK
print(c['B']) # >>> ERROR: 'IntBlock' object has no attribute 'sp_index'
```
#### Problem description

The above code fails, when using mixed type data on segmenting the frame.
A possible work-around is to recast the resulting sparse dataframe to a dense data frame via 
c_new = pd.DataFrame(c)

At the bootom of this it seems that `pandas.concat` always uses the highest class object in the to catenate list, e.g. here the SparseDataFrame, to store teh resulting values in. This effectively works deep, below, however, the on surface interfaces are kind of blown out if the data contained is not as expected.
This might be a very similar issue as #18551 (https://github.com/pandas-dev/pandas/issues/18551)

#### Expected Output

well, hard to say if SparseDataFrames are to handle mixed type data (possibly keeping some of the columns sparse, other dense), or if `pd.concat` should fall back to a dense DataFrame here. Anyhow, the issue should be fixed one way or another,


#### Output of ``pd.show_versions()``
<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.21.0
pytest: 3.0.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.13.3
scipy: 0.19.1
pyarrow: None
xarray: None
IPython: 5.3.0
sphinx: 1.5.6
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.2.2
numexpr: 2.6.2
feather: None
matplotlib: 2.0.2
openpyxl: 2.4.7
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.3
bs4: 4.6.0
html5lib: 0.999
sqlalchemy: 1.1.9
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

</details>
