### Description

Ability to clear or mark task groups as success/failure and have that propagate to the tasks within that task group. Sometimes there is a need to adjust the status of tasks within a task group, which can get unwieldy depending on the number of tasks in that task group. A great quality of life upgrade, and something that seems like an intuitive feature, would be the ability to clear or change the status of all tasks at their taskgroup level through the UI.

### Use case/motivation

In the event a large number of tasks, or a whole task group in this case, need to be cleared or their status set to success/failure this would be a great improvement. For example, a manual DAG run triggered through the UI or the API that has a number of task sensors or tasks that otherwise don't matter for that DAG run - instead of setting each one as success by hand, doing so for each task group would be great.

### Related issues

_No response_

### Are you willing to submit a 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)

input_datasets: dict[Dataset, None] = {}
        
        
        
        
        for dag in dags:
            curr_orm_dag = existing_dags.get(dag.dag_id)
            if not dag.dataset_triggers:
                if curr_orm_dag and curr_orm_dag.schedule_dataset_references:
                    curr_orm_dag.schedule_dataset_references = []
            for dataset in dag.dataset_triggers:
                dag_references[dag.dag_id].add(dataset.uri)
                input_datasets[DatasetModel.from_public(dataset)] = None
            curr_outlet_references = curr_orm_dag and curr_orm_dag.task_outlet_dataset_references
            for task in dag.tasks:
                dataset_outlets = [x for x in task.outlets or [] if isinstance(x, Dataset)]
                if not dataset_outlets:
                    if curr_outlet_references:
                        this_task_outlet_refs = [
                            x
                            for x in curr_outlet_references
                            if x.dag_id == dag.dag_id and x.task_id == task.task_id
                        ]
                        for ref in this_task_outlet_refs:
                            curr_outlet_references.remove(ref)
                for d in dataset_outlets:
                    outlet_references[(task.dag_id, task.task_id)].add(d.uri)
                    outlet_datasets[DatasetModel.from_public(d)] = None
        all_datasets = outlet_datasets
        all_datasets.update(input_datasets)