## Problem Description and Formulation

The problem is a linear programming (LP) optimization problem that aims to minimize the cost of consuming calcium and magnesium from two sources: pork meat and shrimp meat. The constraints are that at least 25 units of calcium and 35 units of magnesium must be consumed daily.

Let's define the variables:
- \(x\): the number of servings of pork meat
- \(y\): the number of servings of shrimp meat

The objective function to minimize is the total cost:
\[ \text{Minimize:} \quad 6.5x + 9y \]

Subject to the constraints:
1. Calcium constraint: \(3x + 5y \geq 25\)
2. Magnesium constraint: \(5x + 9y \geq 35\)
3. Non-negativity constraints: \(x \geq 0, y \geq 0\)

## Gurobi Code

```python
import gurobi

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

    # Define the variables
    x = model.addVar(lb=0, name="pork_meat_servings")
    y = model.addVar(lb=0, name="shrimp_meat_servings")

    # Define the objective function
    model.setObjective(6.5 * x + 9 * y, gurobi.MINIMIZE)

    # Add constraints
    model.addConstr(3 * x + 5 * y >= 25, name="calcium_constraint")
    model.addConstr(5 * x + 9 * y >= 35, name="magnesium_constraint")

    # Optimize the model
    model.optimize()

    # Check if the model is optimized
    if model.status == gurobi.OPTIMAL:
        print("Optimal solution found.")
        print(f"Optimal servings of pork meat: {x.varValue}")
        print(f"Optimal servings of shrimp meat: {y.varValue}")
        print(f"Minimum cost: ${model.objVal:.2f}")
    else:
        print("No optimal solution found.")

# Run the optimization problem
solve_optimization_problem()
```