To solve the optimization problem described, we first need to translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints in terms of these variables.

Let's denote:
- $x_1$ as the milligrams of vitamin B5,
- $x_2$ as the milligrams of zinc.

The objective function to maximize is given by: $5.62x_1 + 5.08x_2$.

The constraints are as follows:
1. The total combined cardiovascular support index from milligrams of vitamin B5 and milligrams of zinc must be greater than or equal to 11: $7x_1 + 8x_2 \geq 11$.
2. $9x_1 - 10x_2 \geq 0$.
3. The total combined cardiovascular support index from milligrams of vitamin B5 and milligrams of zinc has to be equal to or less than 40: $7x_1 + 8x_2 \leq 40$.
4. Since the constraint that the total combined cardiovascular support index should be at most 40 is already included, we don't need to repeat it.
5. $x_1$ must be a whole number (integer).
6. $x_2$ can be any non-negative real number.

Given this, our symbolic representation of the problem in JSON format is:
```json
{
    'sym_variables': [('x1', 'milligrams of vitamin B5'), ('x2', 'milligrams of zinc')],
    'objective_function': '5.62*x1 + 5.08*x2',
    'constraints': [
        '7*x1 + 8*x2 >= 11',
        '9*x1 - 10*x2 >= 0',
        '7*x1 + 8*x2 <= 40'
    ]
}
```

Now, let's implement this problem using Gurobi in Python:
```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.INTEGER, name="milligrams_of_vitamin_B5")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="milligrams_of_zinc", lb=0)

# Set the objective function
m.setObjective(5.62*x1 + 5.08*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(7*x1 + 8*x2 >= 11, "cardiovascular_support_index_min")
m.addConstr(9*x1 - 10*x2 >= 0, "vitamin_zinc_ratio")
m.addConstr(7*x1 + 8*x2 <= 40, "cardiovascular_support_index_max")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Milligrams of Vitamin B5: {x1.x}")
    print(f"Milligrams of Zinc: {x2.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found")
```