### Apache Airflow version

2.7.0

### What happened

When running `BigQueryValueCheckOperator` in deferrable mode, the operator always reports a successful status even if the `pass_value` has not been met. 

### What you think should happen instead

If the value returned by the SQL given to the operator does not equal the `pass_value` then the operator should fail.  This occurs when `deferrable=False` but not when it is `True`.

### How to reproduce

The following DAG code should replicate the issue.  Both tasks provide some SQL that just returns `false` and with a `pass_value` of `True`.  The only difference is the fact that the first task is running in deferrable mode.

```
from datetime import datetime

from airflow import models
from airflow.providers.google.cloud.operators.bigquery import BigQueryValueCheckOperator

with models.DAG(
    dag_id='bq_value_check',
    start_date=datetime(2023, 8, 31),
    catchup=False,
    schedule='0 0 * * *',
) as dag:

    test1 = BigQueryValueCheckOperator(
        task_id=f'test1',
        sql=f'SELECT false;',
        pass_value=True,
        retries=0,
        deferrable=False,
    )

    test2 = BigQueryValueCheckOperator(
        task_id=f'test2',
        sql=f'SELECT false;',
        pass_value=True,
        retries=0,
        deferrable=True,
    )
```

<img width="275" alt="Screenshot 2023-09-01 at 14 42 28" src="https://github.com/apache/airflow/assets/967119/8ac7539e-dee0-45d6-b1da-b5a9f32986b4">


Some log extracts:
**test1**

```
[2023-09-01, 13:34:53 UTC] {bigquery.py:1596} INFO - Inserting job airflow_1693575293576477_43573f106bd19562ebd18f0679e80536
[2023-09-01, 13:34:54 UTC] {taskinstance.py:1824} ERROR - Task failed with exception
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/site-packages/airflow/providers/google/cloud/operators/bigquery.py", line 425, in execute
    super().execute(context=context)
  File "/usr/local/lib/python3.10/site-packages/airflow/providers/common/sql/operators/sql.py", line 857, in execute
    self._raise_exception(error_msg)
  File "/usr/local/lib/python3.10/site-packages/airflow/providers/common/sql/operators/sql.py", line 187, in _raise_exception
    raise AirflowException(exception_string)
airflow.exceptions.AirflowException: Test failed.
Pass value:True
Tolerance:None
Query:
SELECT false;
Results:
[False]
```

**test2**
```
[2023-09-01, 13:34:53 UTC] {bigquery.py:1596} INFO - Inserting job airflow_1693575293256612_92474edc414865ab2efb14bd8b18e24d
[2023-09-01, 13:34:53 UTC] {bigquery.py:446} INFO - Current state of job airflow_1693575293256612_92474edc414865ab2efb14bd8b18e24d is DONE
[2023-09-01, 13:34:53 UTC] {taskinstance.py:1345} INFO - Marking task as SUCCESS. dag_id=bq_value_check, task_id=test2, execution_date=20230831T000000, start_date=20230901T133452, end_date=20230901T133453
[2023-09-01, 13:34:53 UTC] {local_task_job_runner.py:225} INFO - Task exited with return code 0
```

### Operating System

n/a

### Versions of Apache Airflow Providers

apache-airflow-providers-google==10.7.0

### Deployment

Astronomer

### Deployment details

_No response_

### 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 checks against BigQuery.
    This operator expects a SQL query that returns a single row. Each value on
    that row is evaluated using a Python ``bool`` cast. If any of the values
    is falsy, the check errors out.
    .. seealso::
        For more information on how to use this operator, take a look at the guide:
        :ref:`howto/operator:BigQueryCheckOperator`
    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 equals to zero. 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 three standard
    deviation for the 7-day average.
    This operator can be used as a data quality check in your pipeline.
    Depending on where you put it in your DAG, you have the choice to stop the