## Step 1: Define the symbolic representation of the variables
The variables are 'knishes', 'apples', and 'chicken drumsticks', which can be represented symbolically 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 $8.01x_1^2 + 9.79x_1x_2 + 4.17x_2x_3 + 4.92x_2 + 2.03x_3$.

## 3: Define the constraints in symbolic notation
The constraints are:
1. $26x_1 + 3x_2 + 19x_3 \geq 46$ (total combined healthiness rating from all items)
2. $3x_2 + 19x_3 \geq 46$ (total combined healthiness rating from apples and chicken drumsticks)
3. $-4x_1 + 5x_3 \geq 0$ (relationship between knishes and chicken drumsticks)

## 4: Create a symbolic representation of the problem
```json
{
'sym_variables': [('x1', 'knishes'), ('x2', 'apples'), ('x3', 'chicken drumsticks')],
'objective_function': '8.01*x1^2 + 9.79*x1*x2 + 4.17*x2*x3 + 4.92*x2 + 2.03*x3',
'constraints': [
'26*x1 + 3*x2 + 19*x3 >= 46',
'3*x2 + 19*x3 >= 46',
'-4*x1 + 5*x3 >= 0'
]
}
```

## Step 5: Write the Gurobi code to solve the optimization problem
```python
import gurobi

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

    # Define the variables
    knishes = model.addVar(name="knishes", lb=-gurobi.GRB.INFINITY)
    apples = model.addVar(name="apples", lb=-gurobi.GRB.INFINITY)
    chicken_drumsticks = model.addVar(name="chicken_drumsticks", lb=-gurobi.GRB.INFINITY)

    # Define the objective function
    model.setObjective(8.01 * knishes**2 + 9.79 * knishes * apples + 4.17 * apples * chicken_drumsticks + 4.92 * apples + 2.03 * chicken_drumsticks, gurobi.GRB.MINIMIZE)

    # Define the constraints
    model.addConstr(26 * knishes + 3 * apples + 19 * chicken_drumsticks >= 46)
    model.addConstr(3 * apples + 19 * chicken_drumsticks >= 46)
    model.addConstr(-4 * knishes + 5 * chicken_drumsticks >= 0)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Knishes: {knishes.varValue}")
        print(f"Apples: {apples.varValue}")
        print(f"Chicken Drumsticks: {chicken_drumsticks.varValue}")
        print(f"Objective Function Value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```