Of course they are different, because the three interaction terms are different.
In your scheme one, all the data in your centered variable "quant" will turn to 0 if the subject is a male (int1).
In your scheme two, all the data in your centered variable "quant" will change sign if the subject is a male (int2).
In your scheme three, all the data in your centered variable "quant" will be as is if it is from a male, but double if it's from a female (int3).
Now, a couple points for the upcoming discussion:
int1, int2, and int3 are three different variables and they have different means and standard deviations. If you feed a third variable into a regression model, the coefficients of the two per-exisiting predictors' may change. This is extremely common for continuous predictors that are not 100% independent from each other.
Standardized coefficient is just the original coefficient adjusted by the ratio of SD(x) and SD(y): $Standardized\beta x_i = \beta x_i \frac{SD_{xi}}{SD_y}$
Because of the above two points, the final standardized coefficients can change. The standardized coefficient of centeredmeanB changes because you add a different third variable into the model, causing its coefficient to change. Once it changes, the standardized beta also changes even the SD of centeredmenaB remains the same.
The changes in the interaction terms is easier to perceive: they are different variables.
If you use generalized linear model to try this one more time, you should see that scheme number one is the closest to how interaction terms are usually tested. I'd suggest sticking to 1/0 coding for binary variables, the coefficients would make more sense, the intercept makes more sense, and the interaction calculated from it is likely to cause fewer mishaps.
A better advice, though, is to capitalize on the generalize linear model module in SPSS. Users can specify if the variable is categorical or continuous. That way no matter how you code your gender variable, the results will always be consistent.
