You designed a full factorial, but you did not complete a full factorial. That could be leading to your confusing results. Not completing some runs is NOT equivalent to a custom design. Custom designs are optimal for the model that is specified. Missing some of the trials may actually lead to some model terms not being estimable.
The confusion with the model window cannot be answered without understanding the data.
As I said in my first post, a mixture response surface model is to be used only when you have mixture factors. You can ignore it because it does not apply to your situation. If you use it, you will essentially be fitting a no-intercept model which completely changes all of the mathematics and interpretations of your model. Don’t use it.
From your Pareto screen capture, it appears that the last term is not being tested (a missing value for the effect). This leads me to believe that your missing runs are not allowing you to fit the model that you desire. Do you see a Singularity Details part in your JMP report? If so then I am correct. You need to build a model that your data will allow you to estimate.
And finally, the default response surface model in JMP is the default because it is the model associated with your full factorial design. You did not run that full design. Therefore, the model is likely not correct. You changed the design, you need to change your model because those things go together.
Dan Obermiller