To solve the optimization problem described, we first need to convert the natural language description into a symbolic representation. This involves defining variables and formulating the objective function and constraints algebraically.

Let's define:
- $x_1$ as the number of minivans produced,
- $x_2$ as the number of trucks produced.

The profit per minivan is $1200, and the profit per truck is $1700. Thus, the objective function to maximize profit can be written as:
\[ \text{Maximize: } 1200x_1 + 1700x_2 \]

The constraints are based on the availability of the assembly and painting machines. For the assembly machine, it takes 2 hours to make a minivan and 4 hours to make a truck, with a maximum availability of 12 hours per day. This constraint can be written as:
\[ 2x_1 + 4x_2 \leq 12 \]

For the painting machine, it takes 1.5 hours to make a minivan and 2 hours to make a truck, with a maximum availability of 11 hours per day. This constraint can be written as:
\[ 1.5x_1 + 2x_2 \leq 11 \]

Additionally, we have non-negativity constraints since the number of vehicles produced cannot be negative:
\[ x_1 \geq 0, x_2 \geq 0 \]

In symbolic representation with natural language objects, we have:

```json
{
  'sym_variables': [('x1', 'number of minivans'), ('x2', 'number of trucks')],
  'objective_function': '1200*x1 + 1700*x2',
  'constraints': ['2*x1 + 4*x2 <= 12', '1.5*x1 + 2*x2 <= 11', 'x1 >= 0', 'x2 >= 0']
}
```

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

```python
from gurobipy import *

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

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

# Set the objective function
m.setObjective(1200*x1 + 1700*x2, GRB.MAXIMIZE)

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

# Optimize the model
m.optimize()

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