To address the optimization problem described, we first need to establish a symbolic representation of the variables and the constraints provided in the natural language description. The variables are 'milligrams of vitamin D' and 'milligrams of zinc', which can be represented symbolically as $x_0$ and $x_1$, respectively.

Given this, the objective function to minimize is: $1 \cdot x_0 + 7 \cdot x_1$.

The constraints based on the natural language description are:
1. The cardiovascular support index for milligrams of vitamin D is 7: This is a characteristic and not directly a constraint but informs us about the coefficients.
2. Milligrams of vitamin D have an immune support index of 7: Similar to the first point, this informs us about the coefficients.
3. The cardiovascular support index for milligrams of zinc is 1: Again, this is more about characteristics than constraints.
4. Milligrams of zinc each have a immune support index of 2: Characteristics rather than constraints.
5. The total combined cardiovascular support index from milligrams of vitamin D and milligrams of zinc must be 14 or more: $7x_0 + x_1 \geq 14$.
6. The total combined immune support index from milligrams of vitamin D and milligrams of zinc should be 24 at minimum: $7x_0 + 2x_1 \geq 24$.
7. The constraint that the total combined cardiovascular support index should be at minimum 14 is redundant with point 5, so we ignore it for brevity.
8. The same goes for the immune support index being at least 24; it's covered by point 6.
9. $-7x_0 + 9x_1 \geq 0$ represents the constraint involving vitamin D and zinc.
10. The total combined cardiovascular support index from milligrams of vitamin D and milligrams of zinc should be at maximum 26: $7x_0 + x_1 \leq 26$.
11. The total combined immune support index from milligrams of vitamin D and milligrams of zinc should be equal to or less than 32: $7x_0 + 2x_1 \leq 32$.
12. $x_0$ must be an integer (integer constraint for vitamin D).
13. $x_1$ can be any non-negative real number (continuity constraint for zinc).

Thus, the symbolic representation of our problem in a JSON format is:
```json
{
    'sym_variables': [('x0', 'milligrams of vitamin D'), ('x1', 'milligrams of zinc')],
    'objective_function': '1*x0 + 7*x1',
    'constraints': [
        '7*x0 + x1 >= 14',
        '7*x0 + 2*x1 >= 24',
        '-7*x0 + 9*x1 >= 0',
        '7*x0 + x1 <= 26',
        '7*x0 + 2*x1 <= 32'
    ]
}
```

To solve this optimization problem using Gurobi, we can use the following Python code:

```python
from gurobipy import *

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

# Add variables to the model
x0 = m.addVar(vtype=GRB.INTEGER, name="milligrams_of_vitamin_D")
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="milligrams_of_zinc", lb=0)

# Set the objective function
m.setObjective(1*x0 + 7*x1, GRB.MINIMIZE)

# Add constraints to the model
m.addConstr(7*x0 + x1 >= 14, "cardiovascular_support_index_min")
m.addConstr(7*x0 + 2*x1 >= 24, "immune_support_index_min")
m.addConstr(-7*x0 + 9*x1 >= 0, "vitamin_d_zinc_constraint")
m.addConstr(7*x0 + x1 <= 26, "cardiovascular_support_index_max")
m.addConstr(7*x0 + 2*x1 <= 32, "immune_support_index_max")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print("Milligrams of Vitamin D:", x0.x)
    print("Milligrams of Zinc:", x1.x)
else:
    print("No optimal solution found")
```