Performance of merge for categorical index vs category column
#### Code Sample

```python
import pandas
import numpy
import time
numpy.random.seed(11)
N=1_000_000

### 1. Merging on category index (3.85 seconds -slower)
# Create two (random order) dataframes with a column A with unique category dtype set as index
array_1 = numpy.arange(N)
numpy.random.shuffle(array_1)
df_1 = pandas.DataFrame(array_1, columns=["A"])
df_1["B"] = df_1["A"] * 2
df_1["A"] = df_1["A"].astype("category")
df_1 = df_1.set_index("A")

array_2 = array_1.copy()
numpy.random.shuffle(array_2)
df_2 = pandas.DataFrame(array_2, columns=["A"])
df_2["B"] = df_2["A"] * 2
df_2["A"] = df_2["A"].astype("category")
df_2 = df_2.set_index("A")

# merge two dataframes and time
start = time.time()
df_1.merge(df_2, how="left", left_index=True, right_index=True)
print(time.time()-start)

### 2. Merging on category column (0.85 seconds - faster)
# Create two (random order) dataframes with a column A with unique category dtype
array_1 = numpy.arange(N)
numpy.random.shuffle(array_1)
df_1 = pandas.DataFrame(array_1, columns=["A"])
df_1["B"] = df_1["A"] * 2
df_1["A"] = df_1["A"].astype("category")

array_2 = array_1.copy()
numpy.random.shuffle(array_2)
df_2 = pandas.DataFrame(array_2, columns=["A"])
df_2["B"] = df_2["A"] * 2
df_2["A"] = df_2["A"].astype("category")

# merge two dataframes and time
start = time.time()
df_1.merge(df_2, how="left", left_on="A", right_on="A")
print(time.time()-start)
```
#### Problem description

I noticed that when I perform a merge on a unique category dtype column, the merge gets significantly (x4) slower if I set this column as the index. This was unexpected for me as for all other dtypes I have tried (str, int, datetime etc.) the merge is significantly faster when using the indexed column.

I investigated this a bit further. Even though the category column "A" in the above example is unique (no repeated categories), the merge method still calls the `pandas.core.indexes.category.CategoricalIndex.get_indexer_non_unique` method. 

This is because the check `self.is_unique and self.equals(target)` evaluates to False on https://github.com/pandas-dev/pandas/blob/67ee16a283b826b4adb07e5ca64a5206d242acaf/pandas/core/indexes/category.py#L662.  This is because `self.equals(target)` is False https://github.com/pandas-dev/pandas/blob/67ee16a283b826b4adb07e5ca64a5206d242acaf/pandas/core/indexes/category.py#L309. I am not sure if this should be evaluating as True or False? Should two categorical indexes be equal if they have the exact same categories in different orders?

Additionally, I am not sure why get_index_non_unique for a categorical index should be significantly slower than a merge on a categorical column.

Let me know if you have any other insights into why the merge would be slower for a category type when using the index, or if I can provide any additional information / assistance. Thanks for your help!

#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.5.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           : 0.25.3
numpy            : 1.17.3
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 19.3.1
setuptools       : 41.6.0.post20191030
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.10.3
IPython          : 7.9.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.1.1
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : None
sqlalchemy       : None
tables           : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None
</details>
