## 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 vitamin B1'
- $x_2$ represents 'milligrams of vitamin C'
- $x_3$ represents 'milligrams of vitamin B6'

The objective function to maximize is:
\[ 8.69x_1^2 + 7.18x_1x_2 + 5.96x_2^2 + 7.86x_3^2 + 7.79x_1 + 5.08x_2 + 8.92x_3 \]

## Step 2: List the constraints
The constraints given are:
- The kidney support index of $x_1$ is 2: $2x_1 \leq 83$ is not directly given, but $x_0 = 2$ implies a direct assignment rather than a constraint for $x_1$.
- The digestive support index of $x_1$ is 5: $5x_1 \leq 146$.
- The kidney support index of $x_2$ is 7: $7x_2 \leq 83$.
- The digestive support index of $x_2$ is 7: $7x_2 \leq 146$.
- The kidney support index of $x_3$ is 2: $2x_3 \leq 83$.
- The digestive support index of $x_3$ is 5: $5x_3 \leq 146$.
- $x_1^2 + x_2^2 \leq 76$.
- $x_1^2 + x_3^2 \leq 52$.
- $x_1 + x_2 + x_3 \leq 70$ (considering kidney support, but it seems there might be confusion in direct translation).
- $x_2^2 + x_3^2 \leq 85$.
- $x_1^2 + x_3^2 \leq 87$.
- $x_1^2 + x_2^2 + x_3^2 \leq 109$.
- $x_1 + x_2 + x_3 \leq 109$.

However, upon closer inspection, we see that some constraints directly assign values or are not properly translated. The actual constraints from the resources/attributes should be directly translated into mathematical expressions.

## 3: Correctly translate given constraints and objective
Given resources/attributes:
- $r_0$: kidney support index with $x_0 = 2$, $x_1 = 7$, $x_2 = 2$ for $x_1, x_2, x_3$ respectively.
- $r_1$: digestive support index with $x_0 = 5$, $x_1 = 7$, $x_2 = 5$.

This implies:
- Kidney support: $2x_1 + 7x_2 + 2x_3 \leq 83$.
- Digestive support: $5x_1 + 7x_2 + 5x_3 \leq 146$.

And the other constraints as previously listed.

## 4: Formulate the symbolic representation
```json
{
    'sym_variables': [('x1', 'milligrams of vitamin B1'), ('x2', 'milligrams of vitamin C'), ('x3', 'milligrams of vitamin B6')],
    'objective_function': '8.69*x1^2 + 7.18*x1*x2 + 5.96*x2^2 + 7.86*x3^2 + 7.79*x1 + 5.08*x2 + 8.92*x3',
    'constraints': [
        '2*x1 + 7*x2 + 2*x3 <= 83',
        '5*x1 + 7*x2 + 5*x3 <= 146',
        'x1^2 + x2^2 <= 76',
        'x1^2 + x3^2 <= 52',
        'x1 + x2 + x3 <= 70',
        'x2^2 + x3^2 <= 85',
        'x1^2 + x3^2 <= 87',
        'x1^2 + x2^2 + x3^2 <= 109',
        'x1 + x2 + x3 <= 109'
    ]
}
```

## 5: Implement the problem in Gurobi
```python
import gurobi

def optimize_vitamins():
    model = gurobi.Model()
    
    # Define variables
    x1 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="x1")  # milligrams of vitamin B1
    x2 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="x2")  # milligrams of vitamin C
    x3 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="x3")  # milligrams of vitamin B6

    # Objective function
    model.setObjective(8.69*x1**2 + 7.18*x1*x2 + 5.96*x2**2 + 7.86*x3**2 + 7.79*x1 + 5.08*x2 + 8.92*x3, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(2*x1 + 7*x2 + 2*x3 <= 83, name="kidney_support")
    model.addConstr(5*x1 + 7*x2 + 5*x3 <= 146, name="digestive_support")
    model.addConstr(x1**2 + x2**2 <= 76, name="kidney_support_B1_B2")
    model.addConstr(x1**2 + x3**2 <= 52, name="kidney_support_B1_B6")
    model.addConstr(x1 + x2 + x3 <= 70, name="total_kidney_support")
    model.addConstr(x2**2 + x3**2 <= 85, name="digestive_support_B2_B6")
    model.addConstr(x1**2 + x3**2 <= 87, name="digestive_support_B1_B6_2")
    model.addConstr(x1**2 + x2**2 + x3**2 <= 109, name="total_digestive_support")
    model.addConstr(x1 + x2 + x3 <= 109, name="total_digestive_support_2")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin B1: {x1.varValue}")
        print(f"Milligrams of vitamin C: {x2.varValue}")
        print(f"Milligrams of vitamin B6: {x3.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

optimize_vitamins()
```