To solve this problem, we first need to convert the natural language description into a symbolic representation. Let's define the variables as follows:

- $x_1$ represents the number of servings of pork meat consumed.
- $x_2$ represents the number of servings of shrimp meat consumed.

The objective function is to minimize the total cost of the servings, which can be represented algebraically as $6.5x_1 + 9x_2$.

The constraints are:
1. At least 25 units of calcium must be consumed: $3x_1 + 5x_2 \geq 25$.
2. At least 35 units of magnesium must be consumed: $5x_1 + 9x_2 \geq 35$.

Additionally, since we cannot consume a negative number of servings, we have:
3. $x_1 \geq 0$
4. $x_2 \geq 0$

Thus, the symbolic representation in JSON format is:

```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']
}
```

This representation captures the essence of our problem: minimizing the cost of consuming pork and shrimp meat while ensuring we meet the minimum daily requirements for calcium and magnesium.

Now, let's write the Gurobi code in Python to solve this linear programming optimization problem:

```python
from gurobipy import *

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

# Define the variables
x1 = m.addVar(lb=0, name="pork_servings")
x2 = m.addVar(lb=0, name="shrimp_servings")

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

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

# Optimize the model
m.optimize()

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