## Step 1: Define the symbolic representation of the variables
The variables are 'fruit salads', 'bowls of cereal', and 'bowls of pasta', which we can denote as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the objective function into symbolic notation
The objective function to maximize is $1 \cdot x_0 + 7 \cdot x_1 + 2 \cdot x_2$.

## Step 3: Define the constraints in symbolic notation
The constraints are:
1. $7x_0 + 1x_1 + 6x_2 \geq 24$ (total combined sourness index),
2. $6x_1 + 1x_2 \geq 19$ (total combined umami index from bowls of cereal and bowls of pasta),
3. $1x_1 + 6x_2 \leq 96$ (total combined sourness index from bowls of cereal and bowls of pasta),
4. $7x_0 + 1x_1 + 6x_2 \leq 96$ (total combined sourness index from all),
5. $1x_0 + 5x_2 \leq 75$ (total spent on fruit salads and bowls of pasta),
6. $8x_1 + 5x_2 \leq 112$ (total spent on bowls of cereal and bowls of pasta),
7. $1x_0 + 8x_1 + 5x_2 \leq 112$ (total spent on all),
8. $2x_0 + 6x_1 \leq 77$ (total combined umami index from fruit salads and bowls of cereal),
9. $2x_0 + 6x_1 + 1x_2 \leq 77$ (total combined umami index from all).

## 4: Determine the variable types
- $x_0$ (fruit salads) can be fractional,
- $x_1$ (bowls of cereal) must be an integer,
- $x_2$ (bowls of pasta) can be fractional.

## 5: Write down the symbolic representation of the problem
```json
{
'sym_variables': [
    ('x0', 'fruit salads'),
    ('x1', 'bowls of cereal'),
    ('x2', 'bowls of pasta')
],
'objective_function': '1*x0 + 7*x1 + 2*x2',
'constraints': [
    '7*x0 + 1*x1 + 6*x2 >= 24',
    '6*x1 + 1*x2 >= 19',
    '1*x1 + 6*x2 <= 96',
    '7*x0 + 1*x1 + 6*x2 <= 96',
    '1*x0 + 5*x2 <= 75',
    '8*x1 + 5*x2 <= 112',
    '1*x0 + 8*x1 + 5*x2 <= 112',
    '2*x0 + 6*x1 <= 77',
    '2*x0 + 6*x1 + 1*x2 <= 77'
]
}
```

## 6: Implement the problem in Gurobi
```python
import gurobi

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

# Define the variables
x0 = model.addVar(name="fruit_salads", lb=0, ub=None)  # Can be fractional
x1 = model.addVar(name="bowls_of_cereal", lb=0, ub=None, integrality=gurobi.GRB.INTEGER)  # Must be integer
x2 = model.addVar(name="bowls_of_pasta", lb=0, ub=None)  # Can be fractional

# Objective function
model.setObjective(1*x0 + 7*x1 + 2*x2, gurobi.GRB.MAXIMIZE)

# Constraints
model.addConstr(7*x0 + x1 + 6*x2 >= 24, name="sourness_index_total")
model.addConstr(6*x1 + x2 >= 19, name="umami_index_cereal_pasta")
model.addConstr(x1 + 6*x2 <= 96, name="sourness_index_cereal_pasta")
model.addConstr(7*x0 + x1 + 6*x2 <= 96, name="sourness_index_total_max")
model.addConstr(x0 + 5*x2 <= 75, name="cost_fruit_salads_pasta")
model.addConstr(8*x1 + 5*x2 <= 112, name="cost_cereal_pasta")
model.addConstr(x0 + 8*x1 + 5*x2 <= 112, name="cost_total")
model.addConstr(2*x0 + 6*x1 <= 77, name="umami_index_fruit_cereal")
model.addConstr(2*x0 + 6*x1 + x2 <= 77, name="umami_index_total")

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", model.objval)
    print("Fruit Salads: ", x0.varValue)
    print("Bowls of Cereal: ", x1.varValue)
    print("Bowls of Pasta: ", x2.varValue)
else:
    print("The model is infeasible")
```