Is it a problem from a methodological point of view? Sort of, since strictly speaking, SEM assumes that the observed variables are normally distributed, which a fortiori, your likert items are not.
So what to do? You could hold your nose and pretend that everything is normal, trusting in the Central Limit Theorem. I would probably do that, at least as a preliminary, to see if there's anything going on.
A cleaner solution is to use a SEM method adjusted for likert items. Instead of working with the correlation matrix, these methods treat the likert responses as cut points for an underlying continuous variable, whose correlations one then seeks to estimate. Any time I've done this, all variables have had the same number of likert responses, so I don't know if there's an off-the-shelf package for estimating these correlations with discordant likert items. However it should be possible in principle, and it has probably been done in practice somewhere, by someone. If you are using R, you could check out the user group for package lavaan.
In answer to your final question, of course you report all this. In the Methods section of your paper, you will have described the data you are using, including it's Likertship and other issues. You can then explain how you addressed the difficulty.
EDIT. I did some googling and came up with this. There is software that does polychoric correlations with mixed levels. That's what I would advise. Be aware, however, that you need more subjects for polychoric correlations than you would if you could observe the continuous latent variables directly.