Short answer by @bean explains it very well. However, I would like to point to the section 1.1 of the paper Gibbs Sampling for the uninitiated by Resnik and Hardisty which takes the matter to more depth. I am writing few lines from this paper with very slight modifications (This answers repeats few of things which OP knows for sake of completeness)
Formally MLE produces the choice (of model parameter) most likely to generated the observed data.
A MAP estimated is the choice that is most likely given the observed data. In contrast to MLE, MAP estimation applies Bayes's Rule, so that our estimate can take into account
prior knowledge about what we expect our parameters to be in the form of a prior probability distribution.
MLE and MAP estimates are both giving us the best estimate, according to their respective denitions of "best". But notice that using a single estimate -- whether it's MLE or MAP -- throws away information. In principle, parameter could have any value (from the domain); might we not get better estimates if we took the whole distribution into account, rather than just a single estimated value for parameter? If we do that, we're making use of all the information about parameter that we can wring from the observed data, X.
So with this catch, we might want to use none of them. Also, as already mentioned by bean and Tim, if you have to use one of them, use MAP if you got prior. If you do not have priors, MAP reduces to MLE. Conjugate priors will help to solve the problem analytically, otherwise use Gibbs Sampling.