## Step 1: Define the symbolic representation of the problem
Let's denote the number of minivans as $x_1$ and the number of trucks as $x_2$. The profit per minivan is $1200 and per truck is $1700. The assembly machine's time constraint is 2 hours for a minivan and 4 hours for a truck, with a maximum of 12 hours available. The painting machine's time constraint is 1.5 hours for a minivan and 2 hours for a truck, with a maximum of 11 hours available.

## Step 2: Formulate the objective function
The objective function to maximize profit is: $1200x_1 + 1700x_2$.

## 3: Formulate the constraints
The constraints based on the machine hours available are:
1. Assembly machine: $2x_1 + 4x_2 \leq 12$
2. Painting machine: $1.5x_1 + 2x_2 \leq 11$
Additionally, $x_1 \geq 0$ and $x_2 \geq 0$ because the number of vehicles cannot be negative.

## 4: Symbolic representation in the required format
```json
{
'sym_variables': [('x1', 'minivans'), ('x2', 'trucks')],
'objective_function': '1200*x1 + 1700*x2',
'constraints': [
    '2*x1 + 4*x2 <= 12',
    '1.5*x1 + 2*x2 <= 11',
    'x1 >= 0',
    'x2 >= 0'
]
}
```

## 5: Convert the problem into Gurobi code
```python
import gurobi

def solve_vehicle_production():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name="minivans", lb=0, vtype=gurobi.GRB.CONTINUOUS)
    x2 = model.addVar(name="trucks", lb=0, vtype=gurobi.GRB.CONTINUOUS)

    # Define the objective function
    model.setObjective(1200 * x1 + 1700 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(2 * x1 + 4 * x2 <= 12, name="assembly_machine")
    model.addConstr(1.5 * x1 + 2 * x2 <= 11, name="painting_machine")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution:")
        print(f"Minivans: {x1.varValue}")
        print(f"Trucks: {x2.varValue}")
        print(f"Max Profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_vehicle_production()
```