### Apache Airflow version

2.7.1

### What happened

120 DAGs broke when I updated to Airflow 2.7.1. I had to revert to 2.7.0 again, so the tests below are from a local deployment.

The error message is the same for all DAGs:

```python
Broken DAG: [/opt/airflow/dags/dag-name.py] Traceback (most recent call last):
  File "/home/airflow/.local/lib/python3.11/site-packages/airflow/models/dag.py", line 640, in __init__
    self.validate_schedule_and_params()
  File "/home/airflow/.local/lib/python3.11/site-packages/airflow/models/dag.py", line 3289, in validate_schedule_and_params
    raise AirflowException(
airflow.exceptions.AirflowException: DAG Schedule must be None, if there are any required params without default values
```

After testing, I found that if I change the blob_prefix param to "" instead of None, the DAG imports successfully. I have various other params which are None that I will probably need to assign default values for (in reality, the function which Airflow runs handles cases if the value is None, but I guess I need to add a default in the DAG). All 120 DAGs with errors had a field like this in the "params" dictionary I pass to Airflow params.

```python
params = {
    "exclude_pattern": "*errors*",
    "blob_prefix": None,
```

### What you think should happen instead

These are legacy DAGs which have run for a long time without changes (old Airflow instance), so I did not expect an upgrade to break them. 

### How to reproduce

I believe the root cause is passing a param with the value None to the DAG params:

```python
params = {
  subfolder=None,
  conn_id="gcs_conn_id",
}

@dag(
    dag_id="dag-example",
    start_date=datetime(2023, 2, 2, tz="America/New_York"),
    schedule="41 2 * * *",
    params=params,
)
```

### Operating System

Ubuntu 22.04

### Versions of Apache Airflow Providers

Providers info
apache-airflow-providers-celery          | 3.3.3 
apache-airflow-providers-common-sql      | 1.7.1 
apache-airflow-providers-docker          | 3.7.4 
apache-airflow-providers-ftp             | 3.5.1 
apache-airflow-providers-google          | 10.7.0
apache-airflow-providers-http            | 4.5.1 
apache-airflow-providers-imap            | 3.3.1 
apache-airflow-providers-microsoft-azure | 6.3.0 
apache-airflow-providers-mysql           | 5.3.0 
apache-airflow-providers-odbc            | 4.0.0 
apache-airflow-providers-openlineage     | 1.0.2 
apache-airflow-providers-postgres        | 5.6.0 
apache-airflow-providers-redis           | 3.3.1 
apache-airflow-providers-sftp            | 4.6.0 
apache-airflow-providers-sqlite          | 3.4.3 
apache-airflow-providers-ssh             | 3.7.2 

### Deployment

Other Docker-based deployment

### Deployment details

Docker Swarm using docker stack deploy

### Anything else

DAGs fail to import when deployed on 2.7.1

### 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)

"""Get the data interval of this run.
        For compatibility, this method infers the data interval from the DAG's
        schedule if the run does not have an explicit one set, which is possible for
        runs created prior to AIP-39.
        This function is private to Airflow core and should not be depended on as a
        part of the Python API.
        :meta private:
        """
        if run.dag_id is not None and run.dag_id != self.dag_id:
            raise ValueError(f"Arguments refer to different DAGs: {self.dag_id} != {run.dag_id}")
        data_interval = _get_model_data_interval(run, "data_interval_start", "data_interval_end")
        if data_interval is not None:
            return data_interval
        
        
        return self.infer_automated_data_interval(run.execution_date)
    def infer_automated_data_interval(self, logical_date: datetime) -> DataInterval: