## Step 1: Define the symbolic representation of the variables
The variables are 'rotisserie chickens', 'kale salads', and 'bowls of instant ramen', which can be represented symbolically as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the natural language description into a symbolic representation
The symbolic representation of the variables is:
- $x_1$ : 'rotisserie chickens'
- $x_2$ : 'kale salads'
- $x_3$ : 'bowls of instant ramen'

## 3: Define the objective function in symbolic notation
The objective function to minimize is $6x_1 + 1x_2 + 9x_3$.

## 4: Define the constraints in symbolic notation
The constraints are:
- $7x_1 + 20x_2 \geq 23$
- $7x_1 + 20x_2 + 5x_3 \geq 23$
- $5x_2 - 8x_3 \geq 0$

## 5: Consider the healthiness rating constraint and bounds
The healthiness ratings and bounds are given but the critical constraints are already considered above. The bounds for healthiness rating are $r0 \leq 130$, with $x_0 = 7$, $x_1 = 20$, $x_2 = 5$. However, explicit upper bounds for $x_1, x_2, x_3$ are not provided, but we know $x_1, x_2, x_3 \geq 0$ implicitly.

## 6: Formulate the problem in Gurobi
To solve this problem using Gurobi, we will use the Gurobi Python API.

## 7: Write the Gurobi code
```python
import gurobi

# Create a new model
model = gurobi.Model()

# Define the variables
x1 = model.addVar(name="rotisserie_chickens", lb=0)  # rotisserie chickens
x2 = model.addVar(name="kale_salads", lb=0)  # kale salads
x3 = model.addVar(name="bowls_of_instant_ramen", lb=0)  # bowls of instant ramen

# Define the objective function
model.setObjective(6 * x1 + x2 + 9 * x3, gurobi.GRB.MINIMIZE)

# Add constraints
model.addConstr(7 * x1 + 20 * x2 >= 23, name="healthiness_rating_chickens_salads")
model.addConstr(7 * x1 + 20 * x2 + 5 * x3 >= 23, name="healthiness_rating_all")
model.addConstr(5 * x2 - 8 * x3 >= 0, name="kale_salads_ramen_constraint")

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", model.objVal)
    print("Rotisserie Chickens: ", x1.varValue)
    print("Kale Salads: ", x2.varValue)
    print("Bowls of Instant Ramen: ", x3.varValue)
else:
    print("The model is infeasible")
```

## 8: Symbolic Representation
```json
{
    'sym_variables': [('x1', 'rotisserie chickens'), ('x2', 'kale salads'), ('x3', 'bowls of instant ramen')],
    'objective_function': '6*x1 + x2 + 9*x3',
    'constraints': [
        '7*x1 + 20*x2 >= 23',
        '7*x1 + 20*x2 + 5*x3 >= 23',
        '5*x2 - 8*x3 >= 0'
    ]
}
```