You are correct to be suspicious of your results. While indeed it is relatively easy to somewhat simplistically dismiss an AUCROC as "bad" if it is close to $0.50$ (roughly speaking the probability that the model ranks a random positive example more highly than a random negative example), the same rationale is not relevant to the case of AUCPR. That is because the baseline of an AUCPR is not $0.50$ but rather it is dictated by the proportion of positives in our sample. That means that when dealing with an imbalanced sample our actual base-line might extremely low; one can read a more detail exposition on this matter on the CV.SE thread here: What is "baseline" in precision recall curve.
If we want a more informative interpretation of the P-R analysis, we can use what is knows as Precision-Recall Gain curves; these allows us to view the AUCPRG as the expected $F_1$ score. Details on the CV.SE thread gere: Area Under the Precision Recall curve -similar interpretation to AUROC?.
So to recap, a model with AUCROC ~ $90\%$ and AUCPR ~ $40\%$ is not bad, or good for that matter. Without a reference point for performance these numbers do not much match and especially the AUCPR does not lent itself to simple direct interpretations either.