## Step 1: Define the symbolic representation of the problem
Let's denote the number of minivans as $x_1$ and the number of SUVs as $x_2$. The objective is to maximize profit, where each minivan nets $5500 and each SUV nets $4000.

## Step 2: Formulate the objective function
The objective function to maximize profit is $5500x_1 + 4000x_2$.

## 3: Identify the constraints
1. Steel constraint: Both vehicles require 25 kg of steel, and the company receives 1200 kg of steel each week. So, $25x_1 + 25x_2 \leq 1200$.
2. Engineering time constraint: A minivan requires 9 hours of engineering time, and an SUV requires 7 hours. The company has 450 hours available. So, $9x_1 + 7x_2 \leq 450$.
3. Non-negativity constraint: $x_1 \geq 0, x_2 \geq 0$.

## 4: Convert the problem into a symbolic representation
The symbolic representation is:
```json
{
'sym_variables': [('x1', 'minivans'), ('x2', 'SUVs')],
'objective_function': '5500*x1 + 4000*x2',
'constraints': [
    '25*x1 + 25*x2 <= 1200',
    '9*x1 + 7*x2 <= 450',
    'x1 >= 0',
    'x2 >= 0'
]
}
```

## 5: Translate the symbolic representation 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)
    x2 = model.addVar(name='SUVs', lb=0)

    # Define the objective function
    model.setObjective(5500 * x1 + 4000 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(25 * x1 + 25 * x2 <= 1200, name='steel_constraint')
    model.addConstr(9 * x1 + 7 * x2 <= 450, name='engineering_time_constraint')

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal production: {x1.x} minivans, {x2.x} SUVs")
        print(f"Max profit: ${model.objVal:.2f}")
    else:
        print("The problem is infeasible")

solve_vehicle_production()
```