## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'milligrams of vitamin K' and 'milligrams of vitamin D'. Let's denote 'milligrams of vitamin K' as $x_1$ and 'milligrams of vitamin D' as $x_2$. The objective function to maximize is $3x_1 + 6x_2$.

## Step 2: List the constraints in symbolic notation
The constraints given are:
- Kidney support index for $x_1$ is 7, for $x_2$ is 11. The total must be 17 or more: $7x_1 + 11x_2 \geq 17$.
- Energy stability index for $x_1$ is 1, for $x_2$ is 9. The total must be 27 or more: $1x_1 + 9x_2 \geq 27$.
- Immune support index for $x_1$ is 5, for $x_2$ is 5. The total must be greater than or equal to 22: $5x_1 + 5x_2 \geq 22$.
- $-10x_1 + 3x_2 \geq 0$.
- The total combined kidney support index must be 54 or less: $7x_1 + 11x_2 \leq 54$.
- The total combined energy stability index must be 83 or less: $1x_1 + 9x_2 \leq 83$.
- The total combined immune support index must be 28 or less: $5x_1 + 5x_2 \leq 28$.

## 3: Provide the symbolic representation of the problem
The symbolic representation is:
```json
{
'sym_variables': [('x1', 'milligrams of vitamin K'), ('x2', 'milligrams of vitamin D')],
'objective_function': '3*x1 + 6*x2',
'constraints': [
    '7*x1 + 11*x2 >= 17',
    '1*x1 + 9*x2 >= 27',
    '5*x1 + 5*x2 >= 22',
    '-10*x1 + 3*x2 >= 0',
    '7*x1 + 11*x2 <= 54',
    '1*x1 + 9*x2 <= 83',
    '5*x1 + 5*x2 <= 28'
]
}
```

## 4: Write the Gurobi code for the optimization problem
```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name="milligrams_of_vitamin_K", lb=0)  # Assuming non-negative
    x2 = model.addVar(name="milligrams_of_vitamin_D", lb=0)  # Assuming non-negative

    # Define the objective function
    model.setObjective(3*x1 + 6*x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(7*x1 + 11*x2 >= 17, name="kidney_support_index_min")
    model.addConstr(1*x1 + 9*x2 >= 27, name="energy_stability_index_min")
    model.addConstr(5*x1 + 5*x2 >= 22, name="immune_support_index_min")
    model.addConstr(-10*x1 + 3*x2 >= 0, name="vitamin_tradeoff")
    model.addConstr(7*x1 + 11*x2 <= 54, name="kidney_support_index_max")
    model.addConstr(1*x1 + 9*x2 <= 83, name="energy_stability_index_max")
    model.addConstr(5*x1 + 5*x2 <= 28, name="immune_support_index_max")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin K: {x1.varValue}")
        print(f"Milligrams of vitamin D: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```