SEM/CFA in lavaan with nominal exogenous variable

Question

I am trying to fit a measurement model with some latent variables as exonegous variables and a non-latent endogenous variable. Then I use two nominal variables as controls, gender (SPOL) and entrepreneurial family background (DRUZ_PODJ). The values in 'SPOL' are 'female' and 'male', and in 'DRUZ_PODJ' they are 'non_ent_fam' and _'ent_fam'. Both are class 'factor'. Here is the code I am using:

m_whole_c<-'
      SPOT=~SPOT1+SPOT2+SPOT3+SPOT4
      MOTIV=~MOTIV1+MOTIV2+MOTIV3+MOTIV4+MOTIV5
      WORK=~WORK1+WORK2+WORK3
      EC=~1*SPOT+1*MOTIV+1*WORK
      EC~~EC
      
      EAg=~EA1+EA3+EA4+EA5
      EAg~~EAg

      ESEg=~ESE1+ESE2+ESE3
      ESEg~~ESEg
      
      EA_2=~1*EA2
      EA_2~~EA_2
      
      Spol=~SPOL
      Druz_podj=~DRUZ_PODJ
      '


whole_m<- cfa(m_whole_c, data = df1)
summary(whole_m, fit.measures = T, standardized = T)

When I run either the cfa() or sem() functions, I get this message:

Warning: lavaan WARNING:
Could not compute standard errors! The information matrix could
not be inverted. This may be a symptom that the model is not
identified.Warning: lavaan WARNING: could not invert information matrix needed for robust test statistic
Warning: lavaan WARNING: covariance matrix of latent variables
            is not positive definite;
            use lavInspect(fit, "cov.lv") to investigate.

What am I doing wrong? Would it be better to just leave SPOL and DRUZ_PODJ as dichtonomous variables with numerical values 0 and 1, and 1 and 2, respectively? I think that would cut it?

If I remove SPOL and DRUZ_PODJ from the model, it works ok.

I hope someone can help me. BTW, I have checked other answers and found none that would answer my problem. Thanks!