BUG: rank() API produce incorrect result when column types are extension types "Float32" and "Float64"

### Pandas version checks

- [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.

- [X] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.


### Reproducible Example

```python
>>>import pandas as pd
>>>s = pd.Series([5.4954145E+29, -9.791984E-21, 9.3715776E-26, pd.NA, 1.8790257E-28], dtype="Float64")
>>>s.rank(method="min")
0    4.0
1    1.0
2    1.0
3    NaN
4    1.0
dtype: float64
>>>s1 = s.astype("float64")
>>>s1.rank("min")
0    4.0
1    1.0
2    3.0
3    NaN
4    2.0
dtype: float64
```


### Issue Description

From the above example code, we can see different "Float64" type data values,  -9.791984E-21, 9.3715776E-26, and 1.8790257E-28, when we rank them with method="min", they are assigned with same rank. If we cast the type to "float64" and call s.rank(method="min"), we can get the right results.

### Expected Behavior

For "Float64" type data values,  -9.791984E-21, 9.3715776E-26, and 1.8790257E-28, when we rank them with method="min", they should be assigned with different ranks.

### Installed Versions

<details>

>>>pd.__version__
'2.0.0'

</details>
