There is a distinction between a compressor for a fixed dataset and one for an unknown population from which we have a sample. The optimal compressor for the sample may be the single best guess for the population, but that's not what Solomonoff induction does. It begins with a prior that allows all possible programs, and it never assigns all probability to the single optimal compressor, so it has no problem with the all-zeroes example.

But the Hutter prize (of which I'm a big fan) is for ever-more-optimal compressors, and in fact many of the solutions don't generalise to other input data without stripping out various tricks.