To solve this optimization problem, we first need to define the decision variables and the objective function. Let's denote the number of minivans as \(M\) and the number of trucks as \(T\).

The profit per minivan is $1200, and the profit per truck is $1700. Thus, the total profit \(P\) can be represented by the equation:
\[ P = 1200M + 1700T \]

This will serve as our objective function to maximize.

Next, we need to consider the constraints based on the availability of the assembly machine and the painting machine.

1. **Assembly Machine Constraint**: It takes 2 hours to assemble a minivan and 4 hours to assemble a truck. The assembly machine is available for at most 12 hours per day. Therefore, the constraint can be represented as:
\[ 2M + 4T \leq 12 \]

2. **Painting Machine Constraint**: It takes 1.5 hours to paint a minivan and 2 hours to paint a truck. The painting machine is available for at most 11 hours per day. Thus, the constraint can be represented as:
\[ 1.5M + 2T \leq 11 \]

Additionally, we have non-negativity constraints since we cannot produce a negative number of vehicles:
\[ M \geq 0 \]
\[ T \geq 0 \]

Now, let's translate these into Gurobi code in Python:

```python
from gurobipy import *

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

# Define the decision variables
M = m.addVar(name="Minivans", vtype=GRB.INTEGER, lb=0)
T = m.addVar(name="Trucks", vtype=GRB.INTEGER, lb=0)

# Set the objective function to maximize profit
m.setObjective(1200*M + 1700*T, GRB.MAXIMIZE)

# Add constraints
m.addConstr(2*M + 4*T <= 12, name="Assembly_Machine_Constraint")
m.addConstr(1.5*M + 2*T <= 11, name="Painting_Machine_Constraint")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Minivans: {M.x}")
    print(f"Trucks: {T.x}")
    print(f"Total Profit: ${1200*M.x + 1700*T.x:.2f}")
else:
    print("No optimal solution found")
```