## Symbolic Representation

The problem can be converted into a symbolic representation as follows:

Let's denote:
- $x_1$ as the amount of maple syrup produced (in kg)
- $x_2$ as the amount of maple candy produced (in kg)

The objective is to maximize profit, which is given by $20x_1 + 15x_2$.

The constraints are:
- $x_1 \leq 10$ (at most 10 kg of maple syrup)
- $x_2 \leq 12$ (at most 12 kg of maple candy)
- $x_1 \geq 3$ (at least 3 kg of maple syrup)
- $x_2 \geq 5$ (at least 5 kg of maple candy)
- $2x_1 + 2x_2 \leq 20$ (boiling station available for at most 20 hours)

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'maple syrup (kg)'), ('x2', 'maple candy (kg)')],
    'objective_function': '20*x1 + 15*x2',
    'constraints': [
        'x1 <= 10',
        'x2 <= 12',
        'x1 >= 3',
        'x2 >= 5',
        '2*x1 + 2*x2 <= 20'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

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

# Define variables
x1 = model.addVar(lb=3, ub=10, name="maple_syrup")  # kg
x2 = model.addVar(lb=5, ub=12, name="maple_candy")  # kg

# Objective function: maximize profit
model.setObjective(20*x1 + 15*x2, gp.GRB.MAXIMIZE)

# Constraints
model.addConstr(x1 <= 10, name="syrup_limit")
model.addConstr(x2 <= 12, name="candy_limit")
model.addConstr(x1 >= 3, name="syrup_min")
model.addConstr(x2 >= 5, name="candy_min")
model.addConstr(2*x1 + 2*x2 <= 20, name="boiling_station_limit")

# Solve the model
model.optimize()

# Print the results
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal amount of maple syrup: {x1.varValue} kg")
    print(f"Optimal amount of maple candy: {x2.varValue} kg")
    print(f"Maximal profit: ${model.objVal:.2f}")
else:
    print("The model is infeasible.")
```