## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to several constraints. Let's define the symbolic variables:
- $x_1$ represents 'milligrams of calcium'
- $x_2$ represents 'grams of protein'
- $x_3$ represents 'milligrams of vitamin B9'

The objective function to maximize is $9x_1 + 2x_2 + 8x_3$.

## Step 2: Translate the constraints into symbolic notation
The constraints given are:
- The kidney support index for milligrams of calcium is 7.69: $7.69x_1$
- The kidney support index for grams of protein is 6.48: $6.48x_2$
- The kidney support index for milligrams of vitamin B9 is 3.53: $3.53x_3$

Specific constraints:
- $6.48x_2 + 3.53x_3 \geq 47$
- $7.69x_1 + 6.48x_2 + 3.53x_3 \geq 64$
- $6.48x_2 + 3.53x_3 \leq 157$
- $7.69x_1 + 3.53x_3 \leq 158$
- $7.69x_1 + 6.48x_2 \leq 192$
- $7.69x_1 + 6.48x_2 + 3.53x_3 \leq 192$

## 3: Provide the symbolic representation of the problem
```json
{
'sym_variables': [('x1', 'milligrams of calcium'), ('x2', 'grams of protein'), ('x3', 'milligrams of vitamin B9')],
'objective_function': '9*x1 + 2*x2 + 8*x3',
'constraints': [
    '6.48*x2 + 3.53*x3 >= 47',
    '7.69*x1 + 6.48*x2 + 3.53*x3 >= 64',
    '6.48*x2 + 3.53*x3 <= 157',
    '7.69*x1 + 3.53*x3 <= 158',
    '7.69*x1 + 6.48*x2 <= 192',
    '7.69*x1 + 6.48*x2 + 3.53*x3 <= 192'
]
}
```

## 4: Implement the optimization problem using Gurobi
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="milligrams_of_calcium", lb=0)  # No upper bound given
    x2 = model.addVar(name="grams_of_protein", lb=0)   # No upper bound given
    x3 = model.addVar(name="milligrams_of_vitamin_B9", lb=0)  # No upper bound given

    # Define the objective function
    model.setObjective(9 * x1 + 2 * x2 + 8 * x3, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(6.48 * x2 + 3.53 * x3 >= 47)
    model.addConstr(7.69 * x1 + 6.48 * x2 + 3.53 * x3 >= 64)
    model.addConstr(6.48 * x2 + 3.53 * x3 <= 157)
    model.addConstr(7.69 * x1 + 3.53 * x3 <= 158)
    model.addConstr(7.69 * x1 + 6.48 * x2 <= 192)
    model.addConstr(7.69 * x1 + 6.48 * x2 + 3.53 * x3 <= 192)

    # Optimize the model
    model.optimize()

    # Print the status of the optimization
    if model.status == gurobi.GRB.Status.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of calcium: {x1.varValue}")
        print(f"Grams of protein: {x2.varValue}")
        print(f"Milligrams of vitamin B9: {x3.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

optimize_problem()
```