## Symbolic Representation

To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation.

Let's define the variables:
- $x_1$ = Number of small circuit boards
- $x_2$ = Number of large circuit boards

The objective function is to maximize profit:
- Profit from small circuit boards: $5x_1$
- Profit from large circuit boards: $7x_2$
- Total profit: $5x_1 + 7x_2$

The constraints are:
- Drilling machine time: $10x_1 + 15x_2 \leq 600$
- Printing machine time: $15x_1 + 18x_2 \leq 600$
- Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'small circuit boards'), ('x2', 'large circuit boards')],
    'objective_function': '5*x1 + 7*x2',
    'constraints': [
        '10*x1 + 15*x2 <= 600',
        '15*x1 + 18*x2 <= 600',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

# Create a new model
model = gp.Model("circuit_boards")

# Define variables
x1 = model.addVar(name="small_circuit_boards", lb=0, vtype=gp.GRB.CONTINUOUS)
x2 = model.addVar(name="large_circuit_boards", lb=0, vtype=gp.GRB.CONTINUOUS)

# Set objective function
model.setObjective(5*x1 + 7*x2, gp.GRB.MAXIMIZE)

# Add constraints
model.addConstr(10*x1 + 15*x2 <= 600, name="drilling_machine_time")
model.addConstr(15*x1 + 18*x2 <= 600, name="printing_machine_time")

# Solve the model
model.optimize()

# Print the solution
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal solution: x1 = {x1.varValue}, x2 = {x2.varValue}")
    print(f"Maximum profit: {model.objVal}")
else:
    print("No optimal solution found")
```