To solve the optimization problem described, we first need to translate the natural language description into a symbolic representation. This involves defining variables for each of the quantities mentioned in the problem (milligrams of vitamin B6 and milligrams of vitamin B12), expressing the objective function using these variables, and then listing all constraints in terms of these variables.

### Symbolic Representation

Let's define:
- \(x_1\) as the amount of milligrams of vitamin B6,
- \(x_2\) as the amount of milligrams of vitamin B12.

The objective function to maximize is: \(9.01x_1 + 5.56x_2\).

The constraints given are:
1. The immune support index from vitamin B6 is 22, which translates to \(22x_1\).
2. The immune support index from vitamin B12 is 14, which translates to \(14x_2\).
3. The total combined immune support index should be at least 51: \(22x_1 + 14x_2 \geq 51\).
4. Ten times the number of milligrams of vitamin B6 minus ten times the number of milligrams of vitamin B12 must be at least zero: \(10x_1 - 10x_2 \geq 0\).
5. The total combined immune support index should be at most 85: \(22x_1 + 14x_2 \leq 85\).
6. Since the amounts of vitamin B6 and B12 do not have to be integers, both \(x_1\) and \(x_2\) are continuous variables.

### Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'milligrams of vitamin B6'), ('x2', 'milligrams of vitamin B12')],
    'objective_function': '9.01*x1 + 5.56*x2',
    'constraints': [
        '22*x1 + 14*x2 >= 51',
        '10*x1 - 10*x2 >= 0',
        '22*x1 + 14*x2 <= 85'
    ]
}
```

### Gurobi Code

Now, let's write the Gurobi code to solve this optimization problem:

```python
from gurobipy import *

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

# Add variables
x1 = m.addVar(lb=0, name="milligrams_of_vitamin_B6")
x2 = m.addVar(lb=0, name="milligrams_of_vitamin_B12")

# Set the objective function
m.setObjective(9.01*x1 + 5.56*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(22*x1 + 14*x2 >= 51, "total_immunity_min")
m.addConstr(10*x1 - 10*x2 >= 0, "vitamin_b6_over_b12")
m.addConstr(22*x1 + 14*x2 <= 85, "total_immunity_max")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Milligrams of Vitamin B6: {x1.x}")
    print(f"Milligrams of Vitamin B12: {x2.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found.")
```