## Problem Description and Symbolic Representation

The problem requires formulating a linear programming (LP) problem to minimize the cost of consuming calcium and magnesium from pork meat and shrimp meat, given certain constraints on the daily intake of these nutrients.

### Symbolic Representation

Let's define the symbolic variables:
- $x_1$ represents the servings of pork meat
- $x_2$ represents the servings of shrimp meat

The objective is to minimize the total cost: $6.5x_1 + 9x_2$

The constraints are:
- Calcium intake: $3x_1 + 5x_2 \geq 25$
- Magnesium intake: $5x_1 + 9x_2 \geq 35$
- Non-negativity: $x_1 \geq 0, x_2 \geq 0$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'servings of pork meat'), ('x2', 'servings of shrimp meat')],
    'objective_function': '6.5*x1 + 9*x2',
    'constraints': [
        '3*x1 + 5*x2 >= 25',
        '5*x1 + 9*x2 >= 35',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

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

# Define the variables
x1 = model.addVar(name="pork_meat", lb=0)  # Servings of pork meat
x2 = model.addVar(name="shrimp_meat", lb=0)  # Servings of shrimp meat

# Objective function: minimize cost
model.setObjective(6.5 * x1 + 9 * x2, gp.GRB.MINIMIZE)

# Constraints
model.addConstr(3 * x1 + 5 * x2 >= 25, name="calcium_intake")
model.addConstr(5 * x1 + 9 * x2 >= 35, name="magnesium_intake")

# Solve the model
model.optimize()

# Print the solution
if model.status == gp.GRB.OPTIMAL:
    print("Optimal Solution:")
    print(f"Servings of pork meat: {x1.varValue}")
    print(f"Servings of shrimp meat: {x2.varValue}")
    print(f"Total cost: ${model.objVal:.2f}")
else:
    print("No optimal solution found.")
```