Here is the issue: A SEM model does not necessarily work because the estimation algorithm should find a solution, and that is not guaranteed. It is not a matter of software, some models cannot be estimated given the data.
The first model seems to work but it actually fails, because you see that "Note. lavaan WARNING: some estimated lv variances are negative". Negative variances are not a possible solution, so you need to change the model (you can work on the covariances of observed variables, or try to fix some variance to zero, check the modification indexes: the options are endless in SEM). This issue belongs to the Heywood case list, you can check
https://pubmed.ncbi.nlm.nih.gov/34197140/ for some advice).
When comparing multiple groups, things get worse, because the model should be estimable for all groups. For "SEXE", for instance, it seems to work (you get estimates) but in reality, you get negative variances here as well, so also here you need to refine your model.
For AGE, the model is simply not estimable, so it needs to be refined. When you see "sample covariances matrix is not positive-definite" it means that the model has some serious issues. There's a vast literature on how to tackle these issues, but it is something that the software cannot solve (if you run with other software, with the same options, you get the same issue).
The "do not run" option is not a solution. The only thing that it does is to hold the estimation until you finish setting up the model. So, if the intermediate model is not estimable but the final one works, it seems like you have solved the issue. In practice, it is better to stop and handle the issues as they arise building the model.
I'd suggest starting with a simple model and investigating an issue when arises (any warning you get is likely an issue with the model being estimated). When you have a model that works for the full sample, try checking the multi-group analysis. If it does not work, try selecting data for each of the groups and see what problems arise in each group.
In general, when you see "lavaan ERROR: ....", it means that you need to change the model, and the software is not the issue. lavaan R package is very powerful (
https://lavaan.ugent.be/), but some models cannot be estimated nonetheless.