### Apache Airflow Provider(s)

common-sql

### Versions of Apache Airflow Providers

```
apache-airflow-providers-amazon==6.0.0
apache-airflow-providers-apache-hive==4.0.1
apache-airflow-providers-apache-livy==3.1.0
apache-airflow-providers-celery==3.0.0
apache-airflow-providers-cncf-kubernetes==4.4.0
apache-airflow-providers-common-sql==1.3.0
apache-airflow-providers-databricks==3.3.0
apache-airflow-providers-dbt-cloud==2.2.0
apache-airflow-providers-elasticsearch==4.2.1
apache-airflow-providers-ftp==3.1.0
apache-airflow-providers-google==8.4.0
apache-airflow-providers-http==4.0.0
apache-airflow-providers-imap==3.0.0
apache-airflow-providers-microsoft-azure==4.3.0
apache-airflow-providers-postgres==5.2.2
apache-airflow-providers-redis==3.0.0
apache-airflow-providers-sftp==4.1.0
apache-airflow-providers-snowflake==3.3.0
apache-airflow-providers-sqlite==3.2.1
apache-airflow-providers-ssh==3.2.0
```

### Apache Airflow version

2.4.3

### Operating System

Debian Bullseye

### Deployment

Astronomer

### Deployment details

_No response_

### What happened

With the latest version of common-sql provider, the `get_records` from hook is now a ordinary dictionary, causing this KeyError with SqlSensor:
```
[2022-11-29, 00:39:18 UTC] {taskinstance.py:1851} ERROR - Task failed with exception
Traceback (most recent call last):
  File "/usr/local/lib/python3.9/site-packages/airflow/sensors/base.py", line 189, in execute
    poke_return = self.poke(context)
  File "/usr/local/lib/python3.9/site-packages/airflow/providers/common/sql/sensors/sql.py", line 98, in poke
    first_cell = records[0][0]
KeyError: 0
```
I have only tested with Snowflake, I haven't tested it with other databases. Reverting back to 1.2.0 solves the issue.

### What you think should happen instead

It should return an iterable list as usual with the query.

### How to reproduce

```
from datetime import datetime

from airflow import DAG
from airflow.providers.common.sql.sensors.sql import SqlSensor

with DAG(
    dag_id="sql_provider_snowflake_test",
    schedule=None,
    start_date=datetime(2022, 1, 1),
    catchup=False,
):
    t1 = SqlSensor(
        task_id="snowflake_test",
        conn_id="snowflake",
        sql="select 0",
        fail_on_empty=False,
        poke_interval=20,
        mode="poke",
        timeout=60 * 5,
    )
```

### Anything else

_No response_

### Are you willing to submit PR?

- [ ] 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)

"""
    Performs a check against Snowflake. The ``SnowflakeCheckOperator`` expects
    a sql query that will return a single row. Each value on that
    first row is evaluated using python ``bool`` casting. If any of the
    values return ``False`` the check is failed and errors out.
    Note that Python bool casting evals the following as ``False``:
    * ``False``
    * ``0``
    * Empty string (``""``)
    * Empty list (``[]``)
    * Empty dictionary or set (``{}``)
    Given a query like ``SELECT COUNT(*) FROM foo``, it will fail only if
    the count ``== 0``. You can craft much more complex query that could,
    for instance, check that the table has the same number of rows as
    the source table upstream, or that the count of today's partition is
    greater than yesterday's partition, or that a set of metrics are less
    than 3 standard deviation for the 7 day average.
    This operator can be used as a data quality check in your pipeline, and
    depending on where you put it in your DAG, you have the choice to
    stop the critical path, preventing from
    publishing dubious data, or on the side and receive email alerts
    without stopping the progress of the DAG.