## Problem Description and Symbolic Representation

The problem involves maximizing profit by determining the number of bulletproof glass panes and fire-rated glass panes Alpha Glass should produce. The goal is to convert the natural language description into a symbolic representation and then into Gurobi code.

### Symbolic Representation

Let's denote:
- $x_1$ as the number of bulletproof glass panes
- $x_2$ as the number of fire-rated glass panes

The profit per bulletproof glass pane is $12, and the profit per fire-rated glass pane is $9.5. Therefore, the objective function to maximize profit is:
\[ \text{Maximize:} \quad 12x_1 + 9.5x_2 \]

The constraints based on the machine availability are:
- Heating machine: $4x_1 + 7x_2 \leq 350$
- Cooling machine: $6x_1 + 9x_2 \leq 350$
- Non-negativity: $x_1 \geq 0, x_2 \geq 0$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'bulletproof glass panes'), ('x2', 'fire-rated glass panes')],
    'objective_function': '12*x1 + 9.5*x2',
    'constraints': [
        '4*x1 + 7*x2 <= 350',
        '6*x1 + 9*x2 <= 350',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobi

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

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

    # Objective function: Maximize 12*x1 + 9.5*x2
    model.setObjective(12*x1 + 9.5*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(4*x1 + 7*x2 <= 350, name="heating_machine_constraint")
    model.addConstr(6*x1 + 9*x2 <= 350, name="cooling_machine_constraint")

    # Solve the model
    model.optimize()

    # Print the results
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution found. Number of bulletproof glass panes: {x1.varValue}, Number of fire-rated glass panes: {x2.varValue}")
        print(f"Maximum profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_alpha_glass_problem()
```