To solve the given optimization problem, we first need to define the decision variables, objective function, and constraints based on the natural language description provided.

Let's denote:
- \(x_1\) as the number of servings of pork meat consumed.
- \(x_2\) as the number of servings of shrimp meat consumed.

The objective is to minimize the total cost. Given that a serving of pork meat costs $6.5 and a serving of shrimp meat costs $9, the objective function can be written as:
\[ \text{Minimize:} \quad 6.5x_1 + 9x_2 \]

There are two main constraints based on the nutritional requirements:
1. At least 25 units of calcium must be consumed daily. Since pork meat contains 3 units of calcium per serving and shrimp meat contains 5 units of calcium per serving, we have:
\[ 3x_1 + 5x_2 \geq 25 \]

2. At least 35 units of magnesium must be consumed daily. Given that pork meat contains 5 units of magnesium per serving and shrimp meat contains 9 units of magnesium per serving, we get:
\[ 5x_1 + 9x_2 \geq 35 \]

Additionally, \(x_1\) and \(x_2\) should be non-negative since they represent the number of servings of food, which cannot be negative.

Now, let's translate this problem into Gurobi code in Python:

```python
from gurobipy import *

# Create a new model
m = Model("Nutrition_Optimization")

# Define decision variables
x1 = m.addVar(name="pork_servings", vtype=GRB.CONTINUOUS, lb=0)
x2 = m.addVar(name="shrimp_servings", vtype=GRB.CONTINUOUS, lb=0)

# Set objective function: minimize cost
m.setObjective(6.5*x1 + 9*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(3*x1 + 5*x2 >= 25, name="calcium_constraint")
m.addConstr(5*x1 + 9*x2 >= 35, name="magnesium_constraint")

# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Pork servings: {x1.x}")
    print(f"Shrimp servings: {x2.x}")
    print(f"Total cost: ${6.5*x1.x + 9*x2.x:.2f}")
else:
    print("No optimal solution found.")
```