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SmartPLS or PLS algorithm in general need one line (row) of data per observation. If you have a real matched pairs sample then this is possible to analyze with PLS. You have some constructs from the one respondent (e.g., the client) and the other constructs from a matched pair (e.g., the provider). ...

If I understand you correctly you have an experimental design.
You can simply add your design variables (scenarios) as dummy variables (0,1) and regress them on the outcomes while controlling for other variables (age, sex, fashion consciousness.. etc).

Please read the following paper: https://www.researchgate.net/profile/Jan-Michael-Becker/publication/325333754_Estimating_Moderating_Effects_in_PLS-SEM_and_PLSc-SEM_Interaction_Term_GenerationData_Treatment/links/5b0c6f4baca2725783ec44bd/Estimating-Moderating-Effects-in-PLS-SEM-and-PLSc-SEM-Interact...

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SPSS does not offer a PLSSEM algorithm. Thus, the correlations that you generate in SPSS are based on different methods with different assumptions than those in PLSSEM. It is not expected that they are equal.

Yes, that should generally be possible.

Are your sub variables latent as well? Like in a higher-order construct?
Or are your sub variables observed? Then just combine them on one latent variable.

I am not sure whether the weighted PLS will help with this issue. The general procedure would be the following: Build you model with your normal constructs as independent latent variables and the binary response as single item construct. Estimate the PLS model. The results would be similar to a line...

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Yes

You can use the bootstrapping output to calculate the values for all bootstrapping samples and then calculate standard errors and confidence intervals from there

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You can use the PLS-MGA or Permutation test.

If you only want to model them as first-order constructs then there is not need to use the two-stage approach.

The following paper shows that it is advantageous to use path weighting for reflective-formative type models. Often the difference is negligible if the lower-order constructs are not highly correlated. Becker, J. M., Klein, K., & Wetzels, M. (2012). Hierarchical latent variable models in PLS-SEM...