pd.concat doesn't preserve Categorical dtype when the categorical columns is missing in one of the DataFrames.
```python
a = pd.DataFrame({'f1': [1,2,3]})
b = pd.DataFrame({'f1': [2,3,1], 'f2': pd.Series([4,4,4]).astype('category')})

pd.concat((a,b), sort=True).dtypes
>> f1     int64
>> f2    object
>> dtype: object
```
#### Problem description

(Similar to #14016, not sure if it's caused by the same bug or another one. feel free to merge)
When concatenating two DataFrames where one has a categorical column that the other is missing, the result contains the categorical column as a 'object' (losing the "real" dtype).

If we were to fill the missing column with Nones (but with the same categorical dtype), the concatenation would keep the dtype.
In the previous example, adding:

```python
a['f2'] = pd.Series([None, None, None]).astype(b.dtypes['f2'])
```

before concatenating, will solve the problem.

I believe if a field is missing from one of the merged dataframes, a reasonable behavior would be to copy it and preserve its dtype.

#### Expected Output
Column 'f2' should be a categorical (same as b['f2']).

#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Darwin
OS-release: 18.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.23.0
pytest: None
pip: 10.0.1
setuptools: 39.0.1
Cython: None
numpy: 1.14.3
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.4.0
sphinx: None
patsy: None
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 2.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
sqlalchemy: 1.1.13
pymysql: None
psycopg2: 2.7.3.2 (dt dec pq3 ext lo64)
jinja2: 2.9.4
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
