## Step 1: Define the symbolic representation of the problem
The problem involves minimizing an objective function subject to several constraints. The variables are 'kale salads', 'bowls of cereal', and 'sashimi', which we can denote as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to minimize is $1.45x_0 + 2.92x_1 + 6.97x_2$.

## 3: List the constraints in symbolic notation
The constraints given are:
- $10x_0 + 1x_1 + 13x_2 \leq 127$ (grams of fat)
- $1x_0 + 5x_1 + 7x_2 \leq 77$ (grams of carbohydrates)
- $12x_0 + 5x_1 + 12x_2 \leq 73$ (grams of fiber)
- $14x_0 + 10x_1 + 4x_2 \leq 81$ (grams of protein)
- $10x_0 + 1x_1 \geq 28$ (at least 28 grams of fat from kale salads and bowls of cereal)
- $1x_1 + 13x_2 \geq 38$ (at least 38 grams of fat from bowls of cereal and sashimi)
- $10x_0 + 13x_2 \geq 21$ (at least 21 grams of fat from kale salads and sashimi)
- $10x_0 + 1x_1 + 13x_2 \geq 21$ (at least 21 grams of fat from all sources)
- $1x_0 + 5x_1 \geq 11$ (at least 11 grams of carbohydrates from kale salads and bowls of cereal)
- $1x_0 + 7x_2 \geq 14$ (at least 14 grams of carbohydrates from kale salads and sashimi)
- $1x_0 + 5x_1 + 7x_2 \geq 14$ (at least 14 grams of carbohydrates from all sources)
- $12x_0 + 5x_1 \geq 9$ (at least 9 grams of fiber from kale salads and bowls of cereal)
- $12x_0 + 5x_1 + 12x_2 \geq 9$ (at least 9 grams of fiber from all sources)
- $10x_1 + 4x_2 \geq 26$ (at least 26 grams of protein from bowls of cereal and sashimi)
- $14x_0 + 10x_1 \geq 14$ (at least 14 grams of protein from kale salads and bowls of cereal)
- $14x_0 + 10x_1 + 4x_2 \geq 14$ (at least 14 grams of protein from all sources)
- $9x_1 - 4x_2 \geq 0$
- $1x_0 + 5x_1 + 7x_2 \leq 54$ (at most 54 grams of carbohydrates from all sources)
- $14x_0 + 4x_2 \leq 49$ (protein from kale salads and sashimi)

## 4: Define the symbolic variables and constraints for the output
The symbolic representation is:
```json
{
'sym_variables': [('x0', 'kale salads'), ('x1', 'bowls of cereal'), ('x2', 'sashimi')],
'objective_function': '1.45*x0 + 2.92*x1 + 6.97*x2',
'constraints': [
    '10*x0 + 1*x1 + 13*x2 <= 127',
    '1*x0 + 5*x1 + 7*x2 <= 77',
    '12*x0 + 5*x1 + 12*x2 <= 73',
    '14*x0 + 10*x1 + 4*x2 <= 81',
    '10*x0 + 1*x1 >= 28',
    '1*x1 + 13*x2 >= 38',
    '10*x0 + 13*x2 >= 21',
    '10*x0 + 1*x1 + 13*x2 >= 21',
    '1*x0 + 5*x1 >= 11',
    '1*x0 + 7*x2 >= 14',
    '1*x0 + 5*x1 + 7*x2 >= 14',
    '12*x0 + 5*x1 >= 9',
    '12*x0 + 5*x1 + 12*x2 >= 9',
    '10*x1 + 4*x2 >= 26',
    '14*x0 + 10*x1 >= 14',
    '14*x0 + 10*x1 + 4*x2 >= 14',
    '9*x1 - 4*x2 >= 0',
    '1*x0 + 5*x1 + 7*x2 <= 54',
    '14*x0 + 4*x2 <= 49'
]
}
```

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

# Define the model
model = gurobi.Model()

# Define the variables
x0 = model.addVar(name='kale_salads', lb=0, ub=None)
x1 = model.addVar(name='bowls_of_cereal', lb=0, ub=None)
x2 = model.addVar(name='sashimi', lb=0, ub=None, integrality=gurobi.GRB.INTEGER)

# Set the objective function
model.setObjective(1.45*x0 + 2.92*x1 + 6.97*x2, gurobi.GRB.MINIMIZE)

# Add constraints
model.addConstr(10*x0 + 1*x1 + 13*x2 <= 127, name='fat')
model.addConstr(1*x0 + 5*x1 + 7*x2 <= 77, name='carbohydrates')
model.addConstr(12*x0 + 5*x1 + 12*x2 <= 73, name='fiber')
model.addConstr(14*x0 + 10*x1 + 4*x2 <= 81, name='protein')
model.addConstr(10*x0 + 1*x1 >= 28, name='fat_kale_cereal')
model.addConstr(1*x1 + 13*x2 >= 38, name='fat_cereal_sashimi')
model.addConstr(10*x0 + 13*x2 >= 21, name='fat_kale_sashimi')
model.addConstr(10*x0 + 1*x1 + 13*x2 >= 21, name='fat_all')
model.addConstr(1*x0 + 5*x1 >= 11, name='carbohydrates_kale_cereal')
model.addConstr(1*x0 + 7*x2 >= 14, name='carbohydrates_kale_sashimi')
model.addConstr(1*x0 + 5*x1 + 7*x2 >= 14, name='carbohydrates_all')
model.addConstr(12*x0 + 5*x1 >= 9, name='fiber_kale_cereal')
model.addConstr(12*x0 + 5*x1 + 12*x2 >= 9, name='fiber_all')
model.addConstr(10*x1 + 4*x2 >= 26, name='protein_cereal_sashimi')
model.addConstr(14*x0 + 10*x1 >= 14, name='protein_kale_cereal')
model.addConstr(14*x0 + 10*x1 + 4*x2 >= 14, name='protein_all')
model.addConstr(9*x1 - 4*x2 >= 0, name='cereal_sashimi')
model.addConstr(1*x0 + 5*x1 + 7*x2 <= 54, name='carbohydrates_limit')
model.addConstr(14*x0 + 4*x2 <= 49, name='protein_kale_sashimi')

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print('Objective: ', model.objVal)
    print('Kale Salads: ', x0.varValue)
    print('Bowls of Cereal: ', x1.varValue)
    print('Sashimi: ', x2.varValue)
else:
    print('The model is infeasible')
```