## Problem Description and Symbolic Representation

The problem requires finding the optimal amount of pears and broccoli for John to eat in order to minimize his cost while meeting his daily nutritional requirements.

### Symbolic Representation

Let's define the symbolic variables:
- $x_1$ represents the pounds of pears
- $x_2$ represents the pounds of broccoli

The objective function to minimize is the total cost:
- Cost = $6x_1 + 8x_2$

The constraints based on the daily nutritional requirements are:
- Calcium: $2x_1 + 4x_2 \geq 15$
- Potassium: $5x_1 + 3x_2 \geq 20$
- Magnesium: $3x_1 + 6x_2 \geq 17$
- Non-negativity: $x_1 \geq 0, x_2 \geq 0$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'pears'), ('x2', 'broccoli')], 
    'objective_function': '6*x1 + 8*x2', 
    'constraints': [
        '2*x1 + 4*x2 >= 15', 
        '5*x1 + 3*x2 >= 20', 
        '3*x1 + 6*x2 >= 17', 
        'x1 >= 0', 
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

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

# Define the variables
x1 = model.addVar(name="pears", lb=0)  # Pounds of pears
x2 = model.addVar(name="broccoli", lb=0)  # Pounds of broccoli

# Define the objective function
model.setObjective(6 * x1 + 8 * x2, gp.GRB.MINIMIZE)

# Add constraints
model.addConstr(2 * x1 + 4 * x2 >= 15, name="calcium_requirement")
model.addConstr(5 * x1 + 3 * x2 >= 20, name="potassium_requirement")
model.addConstr(3 * x1 + 6 * x2 >= 17, name="magnesium_requirement")

# Solve the model
model.optimize()

# Print the results
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal amount of pears: {x1.varValue} pounds")
    print(f"Optimal amount of broccoli: {x2.varValue} pounds")
    print(f"Minimum cost: ${model.objVal:.2f}")
else:
    print("The model is infeasible.")
```