### Apache Airflow version

Other Airflow 2 version (please specify below)

### What happened

Hello ,

we are facing "504 Gateway Time-out" error when running "tasks run -m" CLI command. We are trying to create Complex DAG and run tasks from cli command. When we are trying to run "tasks run -m" then we received gateway timeout error. 

We also observed high resources spike in web server when we tried to execute this cli command. After looking into further, when we run "task run -m" Airflow CLI command, what it does is it parses the list of DAGs and the parses through the task list. Because this , we observed high resources of webserver and received gateway timeout error. 

### What you think should happen instead

We are expecting that when to execute "tasks run" cli command, it should only parse DAG name and task name provided in the command and not parse DAG lists followed by task list.

### How to reproduce

please follow below steps to reproduce this issue. 

1. we have 900 DAG in airflow environment. 
2. we have created web login token to access web server.
3. after that we tried to run "tasks run" using python script

### Operating System

Amazon linux

### Versions of Apache Airflow Providers

Airflow version 2.2.2

### Deployment

Amazon (AWS) MWAA

### Deployment details

_No response_

### Anything else

_No response_

### Are you willing to submit 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)

if not args:
        raise ValueError("Args should be set")
    if not isinstance(args[0], Namespace):
        raise ValueError(
            f"1st positional argument should be argparse.Namespace instance, but is {type(args[0])}"
        )
def action_cli(func=None, check_db=True):
    def action_logging(f: T) -> T:
        """
        Decorates function to execute function at the same time submitting action_logging
        but in CLI context. It will call action logger callbacks twice,
        one for pre-execution and the other one for post-execution.
        Action logger will be called with below keyword parameters:
            sub_command : name of sub-command
            start_datetime : start datetime instance by utc
            end_datetime : end datetime instance by utc
            full_command : full command line arguments
            user : current user
            log : airflow.models.log.Log ORM instance
            dag_id : dag id (optional)
            task_id : task_id (optional)
            execution_date : execution date (optional)
            error : exception instance if there's an exception
        :param f: function instance
        :return: wrapped function
        """
        @functools.wraps(f)
        def wrapper(*args, **kwargs):