Although for the files shown above you'll get 2 mappers, it can't be guaranteed always. Number of mappers depend upon the number of InputSplits created from the input data. If your files are big you might have more than one mappers.
Partitioning is merely a way to tell which key/value goes to which reducer. If you disable it then you either need some other way to do this or you'll end up with performance degradation, as the inputs to reducers will be uneven. A particular reducer might get all of the input or a particular reducer might get zero input. I can't see any performance gain here. Of course, if you think your custom partitioner fits better into the situation you could definitely do that. But skipping partitioning doesn't sound logical to me. The default partitioning behavior depends on hash
itself. After a mapper emits its output keys are hashed to find out which set of key/value pairs goes to which reducer.
And if your data is already sorted and you want to skip the sorting phase in your MR job, you might find the patch provided in response to this JIRA useful. Issue is not closed yet, but it would definitely help you in getting started.
HTH