Make MLFlow version detection more robust and handles mlflow-skinny
Author: helloworld1
Pull request: yes

# What does this PR do?
MLFlow can be provided from either "mlflow" or "mlflow-skinny" package. See https://pypi.org/project/mlflow/.
This PR updated the version check of mlflow to include "mlflow-skinny" package.
Also make the version check not crashing and fallback to synchronous metrics logging if version is not detected.


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Comments:
Thanks @ArthurZucker for reviewing. The fix #29917 indeed will address the mlflow-skinny problem. In my PR, I made the code more robust in case MLFlow version detection failed and the job will not crash and instead default to not use async metric logging.
I have rebased the code on main branch.

@ArthurZucker The `is_mlflow_available()` is used to check if "mlflow" can be imported regardless which version is install. The version check is used to decide if the async logging is available. I refactor the code to make it `is_mlflow_async_log_available()`  function so it will be very clear on its purpose.

Thanks! Again, I think we should also fix the `is_mlflow_availabl` as it does not take into account skinny. By doing this, it will be easier to then test the version of `self._ml_flow` which will directly be either `mlfow` or `skinny` . 
Does that make sense? 

I am fine with the current changes, but we should go for minimal! 

> Thanks! Again, I think we should also fix the `is_mlflow_availabl` as it does not take into account skinny. By doing this, it will be easier to then test the version of `self._ml_flow` which will directly be either `mlfow` or `skinny` . Does that make sense?
> 
> I am fine with the current changes, but we should go for minimal!

Thanks for reviewing again. The current `is_mlflow_available()` is already taking account of `mlflow` or `mlflow-skinny` without change needed. I have tested this function with both mlflow or skinny. And it works as long as `import mlflow` can work.
```
def is_mlflow_available():
    if os.getenv("DISABLE_MLFLOW_INTEGRATION", "FALSE").upper() == "TRUE":
        return False
    return importlib.util.find_spec("mlflow") is not None
```

The issue addressing here is the version check is the "Async logging" requires `mlflow` or `mlfow-skinny` 2.8.0 or higher. And the version checking requires checking `mlflow` or `mlflow-skinny` package version. The function `is_mlflow_async_log_available()` is a refactor of the current logic of version check. It is already pretty minimal I think and I don't think it is able to be combined with `is_mlflow_available()` since they serves different purposes.



Unrelated failing tests, merging