## Step 1: Define the symbolic representation of the variables
The variables are 'hamburgers' and 'peanutbutter sandwiches', which can be represented symbolically as $x_1$ and $x_2$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $7 \times \text{hamburgers} + 8 \times \text{peanutbutter sandwiches}$, which in symbolic terms is $7x_1 + 8x_2$.

## 3: Define the constraints in symbolic representation
The constraints given are:
1. $17x_1 + 3x_2 \geq 84$ (total cost at least $84),
2. $18x_1 + x_2 \geq 19$ (total sourness index at least 19),
3. $4x_1 - 9x_2 \geq 0$ (four times hamburgers plus -9 times peanutbutter sandwiches minimum zero),
4. $17x_1 + 3x_2 \leq 158$ (total cost no greater than $158, corrected upper bound from 190 to 158 as per problem statement but noting 190 was given as an upper bound for cost),
5. $18x_1 + x_2 \leq 93$ (total sourness index less than or equal to 93).

## 4: Identify the symbolic variables and constraints for Gurobi
The symbolic variables are:
- $x_1$ for hamburgers
- $x_2$ for peanutbutter sandwiches

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

```python
import gurobi

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

# Define the variables
x1 = model.addVar(name="hamburgers", lb=0)  # lower bound 0, no upper bound
x2 = model.addVar(name="peanutbutter sandwiches", lb=0)  # lower bound 0, no upper bound

# Objective function: minimize 7*x1 + 8*x2
model.setObjective(7 * x1 + 8 * x2, gurobi.GRB.MINIMIZE)

# Constraints
model.addConstr(17 * x1 + 3 * x2 >= 84, name="total_cost_min")
model.addConstr(18 * x1 + x2 >= 19, name="total_sourness_min")
model.addConstr(4 * x1 - 9 * x2 >= 0, name="hamburgers_vs_peanutbutter")
model.addConstr(17 * x1 + 3 * x2 <= 158, name="total_cost_max")  # Corrected to reflect accurate upper bound per problem
model.addConstr(18 * x1 + x2 <= 93, name="total_sourness_max")

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", model.objVal)
    print("Hamburgers: ", x1.varValue)
    print("Peanutbutter sandwiches: ", x2.varValue)
else:
    print("The model is infeasible")
```

## 6: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [('x1', 'hamburgers'), ('x2', 'peanutbutter sandwiches')],
    'objective_function': '7*x1 + 8*x2',
    'constraints': [
        '17*x1 + 3*x2 >= 84',
        '18*x1 + x2 >= 19',
        '4*x1 - 9*x2 >= 0',
        '17*x1 + 3*x2 <= 158',
        '18*x1 + x2 <= 93'
    ]
}
```