BUG: join/merge of DataFrame does not keep order of index
- [x] I have checked that this issue has not already been reported.

The issue may be related to issue #34133

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

---



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

```python
import pandas

idx_single = pandas.RangeIndex(10)
idx_double = pandas.Index(idx_single.tolist() + idx_single.tolist())

# idx_double
df1 = pandas.DataFrame(index=idx_double)
df2 = pandas.DataFrame(index=idx_single, data=1, columns=["one", "two"])
print(df1.index)
print(df1.join(df2, how="left", sort=False).index)
print(df1.merge(df2, how="left", right_index=True, left_index=True, sort=False).index)
```

#### Problem description

The index of the joined (or merged) dataframe is not the same as the left hand dataframe even though sort=False.

```
Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int64')
Int64Index([0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9], dtype='int64')
Int64Index([0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9], dtype='int64')
```

#### Expected Output
I would expect the index of the joined/merged dataframe to be the same as the left hand dataframe.

```
Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int64')
Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int64')
Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int64')
```
#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit           : f2c8480af2f25efdbd803218b9d87980f416563e
python           : 3.9.0.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
Version          : 10.0.18362
machine          : AMD64
processor        : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : French_Belgium.1252

pandas           : 1.2.3
numpy            : 1.20.1
pytz             : 2021.1
dateutil         : 2.8.1
pip              : 21.0.1
setuptools       : 49.2.0

</details>
