## Symbolic Representation

To solve the given problem, we first need to convert the natural language description into a symbolic representation. Let's define the variables and the objective function, and then list the constraints.

### Variables
- $x_1$ = Number of solar calculators produced
- $x_2$ = Number of finance calculators produced

### Objective Function
The profit per solar calculator is $12, and the profit per finance calculator is $9. Therefore, the objective function to maximize profit is:
\[ \text{Maximize:} \quad 12x_1 + 9x_2 \]

### Constraints
1. Silicon constraint: $5x_1 + 3x_2 \leq 150$
2. Plastic constraint: $4x_1 + 5x_2 \leq 150$
3. Steel constraint: $2x_1 + 3x_2 \leq 70$
4. Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'solar calculators'), ('x2', 'finance calculators')],
    'objective_function': '12*x1 + 9*x2',
    'constraints': [
        '5*x1 + 3*x2 <= 150',
        '4*x1 + 5*x2 <= 150',
        '2*x1 + 3*x2 <= 70',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name='solar_calculators', lb=0, ub=gurobi.GRB.INFINITY)
    x2 = model.addVar(name='finance_calculators', lb=0, ub=gurobi.GRB.INFINITY)

    # Objective function
    model.setObjective(12 * x1 + 9 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(5 * x1 + 3 * x2 <= 150, name='silicon_constraint')
    model.addConstr(4 * x1 + 5 * x2 <= 150, name='plastic_constraint')
    model.addConstr(2 * x1 + 3 * x2 <= 70, name='steel_constraint')

    # Optimize model
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print('Optimal solution found.')
        print(f'Number of solar calculators: {x1.varValue}')
        print(f'Number of finance calculators: {x2.varValue}')
        print(f'Maximum profit: {model.objVal}')
    else:
        print('No optimal solution found.')

solve_optimization_problem()
```