To solve this optimization problem, we first need to convert the natural language description into a symbolic representation. We'll use 'x0' to represent 'kale salads', 'x1' for 'bowls of cereal', and 'x2' for 'sashimi'. 

Given:
- Objective function: Minimize $1.45x_0 + 2.92x_1 + 6.97x_2$
- Constraints based on the given resources/attributes:
    - Fat from kale salads, bowls of cereal, and sashimi with their respective upper bounds.
    - Carbohydrates from each item with minimum requirements.
    - Fiber from each item with minimum requirements.
    - Protein from combinations of items with minimum requirements.
    - A linear constraint involving bowls of cereal and sashimi.

The symbolic representation is:
```json
{
  'sym_variables': [('x0', 'kale salads'), ('x1', 'bowls of cereal'), ('x2', 'sashimi')],
  'objective_function': 'Minimize 1.45*x0 + 2.92*x1 + 6.97*x2',
  'constraints': [
    '10*x0 + x1 + 13*x2 >= 28', 
    'x1 + 13*x2 >= 38', 
    '10*x0 + 13*x2 >= 21', 
    '10*x0 + x1 + 13*x2 >= 21',
    'x0 + 5*x1 >= 11', 
    'x0 + 7*x2 >= 14', 
    'x0 + 5*x1 + 7*x2 <= 54', 
    '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',
    '14*x0 + 4*x2 <= 49'
  ]
}
```

Now, let's implement this problem using Gurobi in Python:

```python
from gurobipy import *

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

# Create variables
x0 = m.addVar(lb=0, name="kale_salads", vtype=GRB.CONTINUOUS)
x1 = m.addVar(lb=0, name="bowls_of_cereal", vtype=GRB.CONTINUOUS)
x2 = m.addVar(lb=0, name="sashimi", vtype=GRB.INTEGER)

# Objective function
m.setObjective(1.45*x0 + 2.92*x1 + 6.97*x2, GRB.MINIMIZE)

# Constraints
m.addConstr(10*x0 + x1 + 13*x2 >= 28)
m.addConstr(x1 + 13*x2 >= 38)
m.addConstr(10*x0 + 13*x2 >= 21)
m.addConstr(10*x0 + x1 + 13*x2 >= 21)
m.addConstr(x0 + 5*x1 >= 11)
m.addConstr(x0 + 7*x2 >= 14)
m.addConstr(x0 + 5*x1 + 7*x2 <= 54)
m.addConstr(x0 + 5*x1 + 7*x2 >= 14)
m.addConstr(12*x0 + 5*x1 >= 9)
m.addConstr(12*x0 + 5*x1 + 12*x2 >= 9)
m.addConstr(10*x1 + 4*x2 >= 26)
m.addConstr(14*x0 + 10*x1 >= 14)
m.addConstr(14*x0 + 10*x1 + 4*x2 >= 14)
m.addConstr(9*x1 - 4*x2 >= 0)
m.addConstr(14*x0 + 4*x2 <= 49)

# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f'kale_salads: {x0.x}, bowls_of_cereal: {x1.x}, sashimi: {x2.x}')
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```