To solve this optimization problem, we first need to translate the natural language description into a symbolic representation. Let's denote:

- $x_1$ as the number of minivans produced,
- $x_2$ as the number of SUVs produced.

The objective function is to maximize profit, which can be represented as:
\[ 5500x_1 + 4000x_2 \]

Given that each vehicle requires 25 kg of steel and the company receives 1200 kg of steel per week, we have a constraint on steel usage:
\[ 25x_1 + 25x_2 \leq 1200 \]
Simplifying this, we get:
\[ x_1 + x_2 \leq 48 \]

For engineering time, minivans require 9 hours and SUVs require 7 hours, with a total of 450 hours available:
\[ 9x_1 + 7x_2 \leq 450 \]

Additionally, we know that the number of vehicles cannot be negative:
\[ x_1 \geq 0, x_2 \geq 0 \]

Thus, our symbolic representation is:
```json
{
    'sym_variables': [('x1', 'number of minivans'), ('x2', 'number of SUVs')],
    'objective_function': '5500*x1 + 4000*x2',
    'constraints': ['x1 + x2 <= 48', '9*x1 + 7*x2 <= 450', 'x1 >= 0', 'x2 >= 0']
}
```

Now, let's implement this problem using Gurobi in Python:

```python
from gurobipy import *

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

# Create variables
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="minivans")
x2 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="SUVs")

# Set the objective function
m.setObjective(5500*x1 + 4000*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + x2 <= 48, "steel_constraint")
m.addConstr(9*x1 + 7*x2 <= 450, "engineering_time_constraint")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Minivans to produce: {x1.x}")
    print(f"SUVs to produce: {x2.x}")
    print(f"Maximum profit: {m.objVal}")
else:
    print("No optimal solution found")
```