BUG: Type Annotation Inconsistency in read_sql_* Functions 
### 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 
import sqlite3


date_params = {"date_col": {"utc": True}}

with sqlite3.connect("blah") as con:
    # Fails type check.
    df = pd.read_sql_query("SELECT * FROM tablename", con, parse_dates=date_params)
    print(df)
```


### Issue Description

The pandas type annotations for the `parse_dates` argument in `read_sql_table()` and `read_sql_query()` is overly restrictive. It incorrectly causes type checkers to complain when using the `parse_dates` argument to pass keyword arguments to `to_datetime()` as documented [here](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_sql_query.html).

To solve this problem, the annotated type for `parse_date` just needs to be switched from `parse_dates: list[str] | dict[str, str] | None` to `list[str] | dict[str, str] | dict[str, dict[str, Any]] | None`.

This problem is not always visible because the corresponding `pandas-stubs` already does this. The inconsistency appears however in some type checkers when additional stubs are not available or configured though.

To illustrate, take the provided (valid) example and run `pyright` on it (with no arguments). It will output the following.

```
(bug_venv)$ pyright example.py
/home/user/Code/pandas_bug/example.py
/home/user/Code/pandas_bug/example.py:8:10 - error: No overloads for "read_sql_query" match the provided arguments (reportCallIssue)
/home/user/Code/pandas_bug/example.py:8:72 - error: Argument of type "dict[str, dict[str, bool]]" cannot be assigned to parameter "parse_dates" of type "list[str] |dict[str, str] | None" in function "read_sql_query"
Type "dict[str, dict[str, bool]]" is not assignable to type "list[str] | dict[str, str] | None"
"dict[str, dict[str, bool]]" is not assignable to "list[str]"
"dict[str, dict[str, bool]]" is not assignable to "dict[str, str]"
Type parameter "_VT@dict" is invariant, but "dict[str, bool]" is not the same as "str"
Consider switching from "dict" to "Mapping" which is covariant in the value type
"dict[str, dict[str, bool]]" is not assignable to "None" (reportArgumentType)
2 errors, 0 warnings, 0 informations
```

I am more than happy to submit a pull request for this is desired, but thought it best to put in this issue first in case I am missing something.

### Expected Behavior

import pandas as pd 
import sqlite3


date_params = {"date_col": {"utc": True}}

with sqlite3.connect("blah") as con:
    # Type checks correctly
    df = pd.read_sql_query("SELECT * FROM tablename", con, parse_dates=date_params)
    print(df) 

### Installed Versions

<details>

INSTALLED VERSIONS
------------------
commit                : 0691c5cf90477d3503834d983f69350f250a6ff7
python                : 3.12.6
python-bits           : 64
OS                    : Linux
OS-release            : 6.11.2-arch1-1
Version               : #1 SMP PREEMPT_DYNAMIC Fri, 04 Oct 2024 21:51:11 +0000
machine               : x86_64
processor             : 
byteorder             : little
LC_ALL                : None
LANG                  : en_US.UTF-8
LOCALE                : en_US.UTF-8

pandas                : 2.2.3
numpy                 : 2.1.2
pytz                  : 2024.2
dateutil              : 2.9.0.post0
pip                   : 24.2
Cython                : None
sphinx                : None
IPython               : None
adbc-driver-postgresql: None
adbc-driver-sqlite    : None
bs4                   : None
blosc                 : None
bottleneck            : None
dataframe-api-compat  : None
fastparquet           : None
fsspec                : None
html5lib              : None
hypothesis            : None
gcsfs                 : None
jinja2                : None
lxml.etree            : None
matplotlib            : None
numba                 : None
numexpr               : None
odfpy                 : None
openpyxl              : None
pandas_gbq            : None
psycopg2              : None
pymysql               : None
pyarrow               : None
pyreadstat            : None
pytest                : None
python-calamine       : None
pyxlsb                : None
s3fs                  : None
scipy                 : None
sqlalchemy            : None
tables                : None
tabulate              : None
xarray                : None
xlrd                  : None
xlsxwriter            : None
zstandard             : None
tzdata                : 2024.2
qtpy                  : None
pyqt5                 : None
</details>