To solve the given problem, we first need to convert the natural language description into a symbolic representation. This involves defining variables and formulating the objective function and constraints using these variables.

Let's define:
- $x_1$ as the number of servings of milk.
- $x_2$ as the number of servings of cheese.

The objective is to minimize the total cost, which can be represented as $2x_1 + 4x_2$, since a serving of milk costs $2 and a serving of cheese costs $4.

The constraints are:
1. The patient must get at least 100 grams of calcium: $10x_1 + 8x_2 \geq 100$.
2. The patient must get at least 80 grams of Vitamin D: $5x_1 + 6x_2 \geq 80$.
3. Non-negativity constraints, as the number of servings cannot be negative: $x_1 \geq 0$, $x_2 \geq 0$.

Symbolic representation:
```json
{
    'sym_variables': [('x1', 'number of servings of milk'), ('x2', 'number of servings of cheese')],
    'objective_function': '2*x1 + 4*x2',
    'constraints': ['10*x1 + 8*x2 >= 100', '5*x1 + 6*x2 >= 80', 'x1 >= 0', 'x2 >= 0']
}
```

To solve this problem using Gurobi in Python, we'll use the following code:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="servings_of_milk")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="servings_of_cheese")

# Set the objective function
m.setObjective(2*x1 + 4*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(10*x1 + 8*x2 >= 100, "calcium_requirement")
m.addConstr(5*x1 + 6*x2 >= 80, "vitamin_d_requirement")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Servings of milk: {x1.x}")
    print(f"Servings of cheese: {x2.x}")
    print(f"Total cost: ${2*x1.x + 4*x2.x:.2f}")
else:
    print("No optimal solution found")
```