OutputDatasetConfig.register_on_complete registers dataset if the step finish with error

While I was running a pipeline a step finished with **Error**:
AzureMLCompute job failed.
DiskFullError: Disk full while running job. Reduce amount of data accessed, or upgrade VM Sku.

As a result of this step I had defined an OutputDatasetConfig with the properties "as_upload" and "register_on_complete". What I was expecting was not to upload dataset neither register it because the step finished with error, so the output is not right, but the situation was that the dataset was upload an registered, and this implies that a tagged version of the dataset is corrupted.

I recommend not to register a dataset if the step finishes with an error that it's what I would expect from documentation.

Regards

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#### Document Details

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* ID: 02631223-bb1d-f9de-2536-23d753c98508
* Version Independent ID: f524ca56-5419-b233-b67a-a1b3d10408e7
* Content: [azureml.data.output_dataset_config.OutputDatasetConfig class - Azure Machine Learning Python](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.output_dataset_config.outputdatasetconfig?view=azure-ml-py)
* Content Source: [AzureML-Docset/stable/docs-ref-autogen/azureml-core/azureml.data.output_dataset_config.OutputDatasetConfig.yml](https://github.com/MicrosoftDocs/MachineLearning-Python-pr/blob/live/AzureML-Docset/stable/docs-ref-autogen/azureml-core/azureml.data.output_dataset_config.OutputDatasetConfig.yml)
* Service: **machine-learning**
* Sub-service: **core**
* GitHub Login: @DebFro
* Microsoft Alias: **debfro**

Start with the OutputDatasetConfig documentation and the linked AzureML-Docset source, then reproduce a pipeline step that fails while using as_upload and register_on_complete. Trace when upload and registration occur after the failure. Done means failed steps do not upload or register corrupted output datasets, with coverage for the reported error case.

Written by the indexing model from the issue text.