To solve the optimization problem described, we need to define variables and constraints based on the given information. Let's denote:

- $R$ as the number of regular models produced,
- $P$ as the number of premium models produced.

The objective is to maximize profit, with each regular model contributing $200 to the profit and each premium model contributing $500.

Constraints are based on the time required for assembly and software verification:

1. Assembly Time Constraint: $70R + 100P \leq 6000$ (since each regular model requires 70 minutes of assembly and each premium model requires 100 minutes, with a maximum of 6000 minutes available).
2. Software Verification Time Constraint: $25R + 30P \leq 4000$ (since each regular model requires 25 minutes of software verification and each premium model requires 30 minutes, with a maximum of 4000 minutes available).

Now, let's translate this problem into Gurobi code in Python. We will use the Gurobi Python interface to formulate and solve this linear programming problem.

```python
from gurobipy import *

# Create a new model
m = Model("Theta_Electronics")

# Define variables
R = m.addVar(name="Regular_Models", vtype=GRB.INTEGER, lb=0)
P = m.addVar(name="Premium_Models", vtype=GRB.INTEGER, lb=0)

# Set the objective function: Maximize profit
m.setObjective(200*R + 500*P, GRB.MAXIMIZE)

# Add constraints
m.addConstr(70*R + 100*P <= 6000, name="Assembly_Time_Constraint")
m.addConstr(25*R + 30*P <= 4000, name="Software_Verification_Time_Constraint")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Regular Models: {R.x}")
    print(f"Premium Models: {P.x}")
    print(f"Maximum Profit: ${200*R.x + 500*P.x}")
else:
    print("No optimal solution found")
```