No because most knowledge is tacit in nature. Things needs to be practised to be really understood. We're not living in the matrix where you can upload knowledge into your brain, you need to actually practise something to understand how it works.
Not to mention that all knowledge is contextual and dependent on its environment. Doing the same things over again in a different environment may yield different results, old failures, through tiny changes may turn out to actually work now. That's what makes constant experimentation and repetition necessary to rebuilt robust systems.
that's why markets are more effective than central planning, and why evolution is the most robust mechanism on earth. Having twenty companies build the same product is more wasteful than having one committee do it, but only competition, incremental change, and reinvention really are able to explore the entire space.
It has the same problems: the historical context is imperfect and very tedious to understand (for those who even try). Reinventing the wheel is always easier, cognitively (perhaps while being vaguely cognizant of how there was a Roman Republic that fell to dictatorship that then fractured into hundreds of apocalyptic Christian fiefdoms and how there was an incident with this thing called Therac-25 [and maybe crystallizing that cognizance in proper government standards for medical software]).
Also, since the bottleneck is brain bandwidth, it's not helpful that old code is written in old languages which were formulated in a time where the most salient bottleneck was memory/processing speed.
These will most likely never see another printing, granted. But generalizing across all books published this way, I'm sure this situation will actually arise in the future.
Doing this is ok. We are also rewriting the same books all over again. Knowledge is forgotten and reinvented and rediscovered. You see this in computer science and programming all the time, but also in other fields. Rather than the programs themselves, preserving the concepts and learnings is much more important. Don’t get me wrong, I think vintage computing is great, but we shouldn’t necessarily solve todays problems with it.