### Description

#27647 has introduced filtering for invalid schemas in Hive hook based on the characters `;` and `!`. I'm wondering if a more generic filtering could be introduced, e.g. one that adheres to the regex `[^a-z0-9_]`, since Hive schemas (and table names) can only contain alphanumeric characters and the character `_`.

Note: since the Hive metastore [stores schemas and tables in lowercase](https://stackoverflow.com/questions/57181316/how-to-keep-column-names-in-camel-case-in-hive/57183048#57183048), checking against `[^a-z0-9_]` is probably better than `[^a-zA-Z0-9_]`.

### Use case/motivation

Ensure that Hive schemas used in `apache-airflow-providers-apache-hive` hooks contain no invalid characters.

### Related issues

_No response_

### Are you willing to submit a PR?

- [X] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

self,
        sql: str,
        schema: str = "default",
        hive_conf: dict[Any, Any] | None = None,
        **kwargs,
    ) -> pandas.DataFrame:
        """
        Get a pandas dataframe from a Hive query
        :param sql: hql to be executed.
        :param schema: target schema, default to 'default'.
        :param hive_conf: hive_conf to execute alone with the hql.
        :param kwargs: (optional) passed into pandas.DataFrame constructor
        :return: result of hive execution
        >>> hh = HiveServer2Hook()
        >>> sql = "SELECT * FROM airflow.static_babynames LIMIT 100"
        >>> df = hh.get_pandas_df(sql)
        >>> len(df.index)
        100
        :return: pandas.DateFrame
        """
        res = self.get_results(sql, schema=schema, hive_conf=hive_conf)
        df = pandas.DataFrame(res["data"], columns=[c[0] for c in res["header"]], **kwargs)
        return df