## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of vitamin B1', 'milligrams of iron', and 'milligrams of vitamin K'. Let's denote them as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $1.72x_1^2 + 6.87x_1x_2 + 3.8x_1x_3 + 1.32x_2^2 + 2.8x_2x_3 + 6.88x_3^2 + 4.73x_1 + 2.35x_2 + 7.32x_3$.

## 3: Define the constraints in symbolic notation
The constraints are:
1. $14x_1 + 5x_2 + 6x_3 \leq 132$ (kidney support index constraint)
2. $14^2x_1^2 + 5^2x_2^2 \geq 34$ (minimum combined kidney support index from $x_1^2$ and $x_2^2$)
3. $14^2x_1^2 + 6^2x_3^2 \geq 25$ (minimum combined kidney support index from $x_1^2$ and $x_3^2$)
4. $14x_1 + 5x_2 + 6x_3 \geq 25$ (minimum total combined kidney support index)
5. $6x_2 - 3x_3 \geq 0$ (linear constraint)

## 4: Express the problem in the required JSON format
```json
{
    'sym_variables': [
        ('x1', 'milligrams of vitamin B1'), 
        ('x2', 'milligrams of iron'), 
        ('x3', 'milligrams of vitamin K')
    ], 
    'objective_function': '1.72*x1^2 + 6.87*x1*x2 + 3.8*x1*x3 + 1.32*x2^2 + 2.8*x2*x3 + 6.88*x3^2 + 4.73*x1 + 2.35*x2 + 7.32*x3', 
    'constraints': [
        '14*x1 + 5*x2 + 6*x3 <= 132', 
        '196*x1^2 + 25*x2^2 >= 34', 
        '196*x1^2 + 36*x3^2 >= 25', 
        '14*x1 + 5*x2 + 6*x3 >= 25', 
        '6*x2 - 3*x3 >= 0'
    ]
}
```

## Step 5: Implement the optimization problem using Gurobi
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="x1", lb=-gurobi.GRB.INFINITY)  # milligrams of vitamin B1
    x2 = model.addVar(name="x2", lb=-gurobi.GRB.INFINITY)  # milligrams of iron
    x3 = model.addVar(name="x3", lb=-gurobi.GRB.INFINITY)  # milligrams of vitamin K

    # Objective function
    model.setObjective(1.72*x1**2 + 6.87*x1*x2 + 3.8*x1*x3 + 1.32*x2**2 + 2.8*x2*x3 + 6.88*x3**2 + 4.73*x1 + 2.35*x2 + 7.32*x3, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(14*x1 + 5*x2 + 6*x3 <= 132)
    model.addConstr(196*x1**2 + 25*x2**2 >= 34)
    model.addConstr(196*x1**2 + 36*x3**2 >= 25)
    model.addConstr(14*x1 + 5*x2 + 6*x3 >= 25)
    model.addConstr(6*x2 - 3*x3 >= 0)

    # Optimize the model
    model.optimize()

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

solve_optimization_problem()
```