BUG: `pd.concat` produces frames with inconsistent order when concating the ones with categorical indices
### 

- [X] I have checked that this issue has not already been reported.

- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.

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


### Reproducible Example

```python
import pandas

df1 = pandas.DataFrame(
    {"col1": ["a_val", "b_val", "c_val"]},
    index=pandas.CategoricalIndex(["a", "b", "c"], categories=["a", "b", "c"]),
)
df2 = pandas.DataFrame(
    {"col1": ["b_val", "a_val", "c_val"]},
    # The same categories as in the first frame, but the values are in a different order
    index=pandas.CategoricalIndex(["b", "a", "c"], categories=["b", "a", "c"]),
)

print(pandas.concat([df1, df2]))

# Output:
#     col1
# a  a_val
# b  b_val
# c  c_val
# a  b_val <-- index doesn't math the original value
# b  a_val <-- index doesn't math the original value
# c  c_val
```


### Issue Description

When concatenating frames with categorical indices that have the same values but they are in a different order, the order of indices does not match the order of values from the original frame. 

In the reproducer above each "x" index value corresponds to the "x_val" value in the frame ("a" -> "a_val", "b" -> "b_val", ...), but in the concated frame this order is broken, the index values appears to be sorted, when the actual frame's values don't.

### Expected Behavior

An expected result would be either properly sorted result:
```python
    col1
a  a_val
b  b_val
c  c_val
a  a_val
b  b_val
c  c_val
```
or the one that is unsorted at all:
```python
    col1
a  a_val
b  b_val
c  c_val
b  b_val
a  a_val
c  c_val

```

### Installed Versions

<details>

INSTALLED VERSIONS
------------------
commit           : 945c9ed766a61c7d2c0a7cbb251b6edebf9cb7d5
python           : 3.8.10.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.4.0-65-generic
Version          : #73-Ubuntu SMP Mon Jan 18 17:25:17 UTC 2021
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.3.4
numpy            : 1.21.2
pytz             : 2021.1
dateutil         : 2.8.2
pip              : 21.2.4
setuptools       : 58.0.4
Cython           : None
pytest           : 6.2.5
hypothesis       : None
sphinx           : 4.2.0
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 3.0.1
IPython          : None
pandas_datareader: None
bs4              : 4.10.0
bottleneck       : None
fsspec           : 2021.08.1
fastparquet      : None
gcsfs            : None
matplotlib       : 3.4.3
numexpr          : None
odfpy            : None
openpyxl         : 3.0.8
pandas_gbq       : None
pyarrow          : 3.0.0
pyxlsb           : None
s3fs             : 2021.08.1
scipy            : None
sqlalchemy       : 1.4.23
tables           : None
tabulate         : None
xarray           : 0.19.0
xlrd             : 2.0.1
xlwt             : None
numba            : None


</details>
