control state of individual taskflow in mapped task-group

^{Originally posted by **ntnhaatj** August 20, 2023}
### Description

Hi, as [my issue was raised here](https://github.com/apache/airflow/issues/25032#issuecomment-1684926654) which also included depth-first execution model context, I would like to open an issue to have more discussion on this matter.

### Use case/motivation

motivated from the depth-first execution model implemented in mapped TaskGroup:

```
┌──────────────┐ ┌────────────────┐ ┌───────────┐
┌──►│extract_file_1│►│transform_file_1│►│load_file_1│
│ └──────────────┘ └────────────────┘ └───────────┘
│
│ ┌──────────────┐ ┌────────────────┐ ┌───────────┐
┌─────────────┐ ├──►│extract_file_2│►│transform_file_2│►│load_file_2│
│get_file_list├─┤ └──────────────┘ └────────────────┘ └───────────┘
└─────────────┘ │
│ ... ... ...
│
│ ┌──────────────┐ ┌────────────────┐ ┌───────────┐
└──►│extract_file_N│►│transform_file_n│►│load_file_N│
└──────────────┘ └────────────────┘ └───────────┘
```

At present, the upstream / downstream list dependencies now only applied on DAGNode (which is the whole TaskGroup or Operator)

There might be better control over desired TaskFlow if we could enhance support for deeper upstream/downstream dependencies at the granularity of mapped task instances, instead of applying them to the entire TaskGroup.

In the model above, `extract_file_1` task downstream list should be only `transform_file_1` and `load_file_1` in order, rather than encompassing the whole mapped group `transform_file[]` and `load_file[]` as in the current implementation.

For instance, I scheduled my test DAG on `Airflow 2.7.0`:

```python
from airflow.decorators import dag, task_group, task
from airflow.operators.empty import EmptyOperator
from pendulum import datetime

files = ["a", "b", "c"]

@dag(start_date=datetime(2022, 12, 1), schedule=None, catchup=False)
def task_group_mapping_example():
@task_group(group_id="etl")
def etl_pipeline(file):
e = EmptyOperator(task_id="e")
t = EmptyOperator(task_id="t")
l = EmptyOperator(task_id="l")

e >> t >> l

etl = etl_pipeline.expand(file=files)
etl

task_group_mapping_example()
```

Clear etl.e[1] state with downstream will trigger all mapped task in `etl.t[]` and `etl.l[]` group.

Thanks,

### 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)