### 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)

tuple_in_condition,
    with_row_locks,
)
from airflow.utils.state import DagRunState, TaskInstanceState
from airflow.utils.trigger_rule import TriggerRule
from airflow.utils.types import NOTSET, ArgNotSet, DagRunType, EdgeInfoType
if TYPE_CHECKING:
    from types import ModuleType
    from pendulum.tz.timezone import Timezone
    from sqlalchemy.orm.query import Query
    from sqlalchemy.orm.session import Session
    from airflow.datasets import Dataset
    from airflow.decorators import TaskDecoratorCollection
    from airflow.models.dagbag import DagBag
    from airflow.models.operator import Operator
    from airflow.models.slamiss import SlaMiss
    from airflow.serialization.pydantic.dag import DagModelPydantic
    from airflow.typing_compat import Literal
    from airflow.utils.task_group import TaskGroup
log = logging.getLogger(__name__)
DEFAULT_VIEW_PRESETS = ["grid", "graph", "duration", "gantt", "landing_times"]
ORIENTATION_PRESETS = ["LR", "TB", "RL", "BT"]
TAG_MAX_LEN = 100
DagStateChangeCallback = Callable[[Context], None]
ScheduleInterval = Union[None, str, timedelta, relativedelta]
ScheduleIntervalArg = Union[ArgNotSet, ScheduleInterval]
ScheduleArg = Union[ArgNotSet, ScheduleInterval, Timetable, Collection["Dataset"]]
SLAMissCallback = Callable[["DAG", str, str, List["SlaMiss"], List[TaskInstance]], None]
DEFAULT_SCHEDULE_INTERVAL = timedelta(days=1)
class InconsistentDataInterval(AirflowException):