To solve the optimization problem described, we first need to translate the natural language description into a symbolic representation. This involves defining variables for each object (knishes, apples, chicken drumsticks), formulating the objective function with these variables, and then listing all constraints using these variables.

Let's define:
- \(x_1\) as the number of knishes,
- \(x_2\) as the quantity of apples,
- \(x_3\) as the quantity of chicken drumsticks.

The objective function to minimize is given by:
\[8.01(x_1)^2 + 9.79(x_1)(x_2) + 4.17(x_2)(x_3) + 4.92(x_2) + 2.03(x_3)\]

Constraints are as follows:
1. Healthiness rating of each item: \(26x_1\) for knishes, \(3x_2\) for apples, and \(19x_3\) for chicken drumsticks.
2. Total combined healthiness rating from apples and chicken drumsticks should be at least 46: \(3x_2 + 19x_3 \geq 46\).
3. Total combined healthiness rating from all items should be at least 46: \(26x_1 + 3x_2 + 19x_3 \geq 46\).
4. The expression minus four times the number of knishes, plus five times the number of chicken drumsticks has to be at minimum zero: \(-4x_1 + 5x_3 \geq 0\).

All variables are continuous (can be fractional), so we don't have any integer constraints.

Here is the symbolic representation:
```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': [
        '3*x2 + 19*x3 >= 46', 
        '26*x1 + 3*x2 + 19*x3 >= 46', 
        '-4*x1 + 5*x3 >= 0'
    ]
}
```

Now, let's write the Gurobi code to solve this optimization problem:
```python
from gurobipy import *

# Create a new model
m = Model("Optimization_Problem")

# Define variables
x1 = m.addVar(lb=0, name="knishes")  # Number of knishes
x2 = m.addVar(lb=0, name="apples")   # Quantity of apples
x3 = m.addVar(lb=0, name="chicken_drumsticks")  # Quantity of chicken drumsticks

# Set the objective function
m.setObjective(8.01*x1**2 + 9.79*x1*x2 + 4.17*x2*x3 + 4.92*x2 + 2.03*x3, GRB.MINIMIZE)

# Add constraints
m.addConstr(3*x2 + 19*x3 >= 46, name="healthiness_apples_chicken")
m.addConstr(26*x1 + 3*x2 + 19*x3 >= 46, name="total_healthiness")
m.addConstr(-4*x1 + 5*x3 >= 0, name="knishes_chicken_constraint")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Knishes: {x1.x}")
    print(f"Apples: {x2.x}")
    print(f"Chicken Drumsticks: {x3.x}")
else:
    print("No optimal solution found.")
```