`fillna` using columns of dtype `category` also fills non-NaN values
#### Code Sample, a copy-pastable example if possible

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

dct = {
    'A': ['a', 'b', 'c', 'b'], 
    'B': ['d', 'e', np.nan, np.nan]
}
df = pd.DataFrame.from_dict(dct).astype('category')
df['C'] = df['B']
df['C'].cat.add_categories(df['A'].cat.categories, inplace=True)
df['C'] = df['C'].fillna(df['A'])
```

**output**


|   | A | B | C
| :---: | ---: | ---: | ---:
| **0** | a | d | a
| **1** | b | e | b
| **2** | c | NaN | c
| **3** | b | NaN | b


#### Problem description

I have two columns, A and B,  of dtype `category`. Column B contains `NaN` values.
Applying `fillna` to B using A (after adding categories in A to categories in B), results in ALL values of B being overwritten with values of A. The issue is that `fillna` also fills non-`NaN` values.

#### Expected Output

Non-`NaN` values should not be overwritten:

|   | A | B | C
| :---: | ---: | ---: | ---:
| **0** | a | d | d
| **1** | b | e | e
| **2** | c | NaN | c
| **3** | b | NaN | b

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.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: 0.24.2
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: None
xarray: None
IPython: 7.4.0
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: 3.0.3
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>
