BUG: pd.replace changes category dtype to object when using a dict to replace values
- [x] I have checked that this issue has not already been reported.

- [x] I have confirmed this bug exists on the latest version of pandas.

- [x] (optional) I have confirmed this bug exists on the master branch of pandas.

---

**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.

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

```python
>>> import pandas as pd
>>> pd.__version__
'1.0.5'
>>> input_dict = {"col1": ["a"], "col2": ["obj1"], "col3": ["cat1"]}
>>> input_df = pd.DataFrame(data=input_dict).astype({"col1": "category", "col2": "category", "col3": "category"})
>>> input_df
  col1  col2  col3
0    a  obj1  cat1
>>> input_df.dtypes
col1    category
col2    category
col3    category
dtype: object
>>> input_df = input_df.replace({"a": "z", "obj1": "obj9", "cat1": "catX"})
>>> input_df
  col1  col2  col3
0    z  obj9  catX
>>> input_df.dtypes
col1    object
col2    object
col3    object
dtype: object

```

#### Problem description

Currently, the category dtypes are lost when we use pandas replace. This loss of category dtype is observed only when a dictionary is used to replace multiple values.

I was able to recreate the issue for `pd.__version__ == '1.1.0.dev0+2073.g280efbfcc'` as well. Also, please see related issue: https://github.com/pandas-dev/pandas/issues/23305

#### Expected Output

I would expect the categorical columns to keep their category dtype after replace is called on the dataframe. Thanks!

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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.3.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 19.5.0
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : en_US.UTF-8

pandas           : 1.0.5
numpy            : 1.19.0
pytz             : 2020.1
dateutil         : 2.8.1
pip              : 19.0.3
setuptools       : 40.8.0
Cython           : None
pytest           : 5.4.3
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : None
IPython          : None
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : None
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : 5.4.3
pyxlsb           : None
s3fs             : None
scipy            : None
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None
numba            : None

</details>
