I am not a huge fan of Bayes factors and so I don’t know a lot about them, but I skimmed through the references and this seems to be expected - the contr.bayes function changes the design matrix (the way factors are coded) from standard zero-or-one dummy coding to something else, so the coefficients should change (and their interpretation as well - actually not sure how to intepret them in this case). I also understand why you might need that correction to get something useful from Bayes factors with fixed effects. I would add that up to a host of other things where Bayes factors are unintuitive :-). I think the problem would not apply for varying effects (e.g. 1|Discount) and so those should be easier to use in this way. If you are unsure on how to work with Bayes factors, I would suggest you just interpret the 95% and 50% posterior intervals for your parameters (unless your reviewer/boss/… makes you to compute Bayes factors). Posterior intervals require little special care and are relatively intuitive, and I think it is vital to work with a tool you understand.
I’ve also recently answered a similar inquiry where I go a bit more into possible approaches when you need something similar to hypothesis testing in Bayesian contest:
Hope that helps!