## Step 1: Define the symbolic representation of the problem
The symbolic representation of the problem consists of pairs of variables in symbolic notation and their corresponding natural language objects, the objective function rendered as an algebraic term, and the list of semi-algebraic constraints.

## Step 2: List the variables and their corresponding symbolic notation
Let's denote the variables as follows:
- 'bagged salads': 'x0'
- 'strips of bacon': 'x1'
- 'kiwis': 'x2'
- 'cornichons': 'x3'
- 'fruit salads': 'x4'
- 'bananas': 'x5'
- 'eggs': 'x6'

## Step 3: Formulate the objective function
The objective function to minimize is: 
2.18*x0 + 5.08*x1 + 8.26*x2 + 8.58*x3 + 4.98*x4 + 5.83*x5 + 4.0*x6

## Step 4: List the constraints
Constraints include:
- Fiber constraints: 
  - 16*x0 + 18*x1 + 19*x2 + 15*x3 + 11*x4 + 10*x5 + 28*x6 >= 62
  - 18*x1 + 19*x2 + 15*x3 + 11*x4 + 10*x5 >= 77
  - 11*x4 + 10*x5 + 28*x6 >= 63
  - 16*x0 + 28*x6 >= 34
  - 16*x0 + 15*x3 >= 52
  - 19*x2 + 11*x4 + 10*x5 >= 28
  - 15*x3 + 11*x4 >= 65
  - 19*x2 + 11*x4 + 10*x5 >= 68
  - 16*x0 + 19*x2 + 11*x4 >= 68
  - 18*x1 + 19*x2 + 10*x5 >= 68
  - 18*x1 + 15*x3 + 10*x5 >= 68
  - 19*x2 + 11*x4 + 10*x5 >= 50
  - 16*x0 + 19*x2 + 11*x4 >= 50
  - 18*x1 + 19*x2 + 10*x5 >= 50
  - 18*x1 + 15*x3 + 10*x5 >= 50
  - 19*x2 + 11*x4 + 10*x5 >= 43
  - 16*x0 + 19*x2 + 11*x4 >= 43
  - 18*x1 + 19*x2 + 10*x5 >= 43
  - 18*x1 + 15*x3 + 10*x5 >= 43
  - 19*x2 + 11*x4 + 10*x5 >= 64
  - 16*x0 + 19*x2 + 11*x4 >= 64
  - 18*x1 + 19*x2 + 10*x5 >= 64
  - 18*x1 + 15*x3 + 10*x5 >= 64
  - 16*x0 + 18*x1 + 19*x2 + 15*x3 + 11*x4 + 10*x5 + 28*x6 >= 64

- Healthiness rating constraints:
  - 5*x0 + 21*x1 + 14*x2 + 27*x3 + 34*x4 + 18*x5 + 14*x6 >= 113
  - 5*x0 + 27*x3 >= 66
  - 14*x2 + 34*x4 >= 104
  - 18*x5 + 14*x6 >= 122
  - 21*x1 + 14*x2 >= 105
  - 21*x1 + 34*x4 >= 95
  - 5*x0 + 34*x4 >= 126
  - 5*x0 + 21*x1 + 14*x6 >= 75
  - 5*x0 + 34*x4 + 14*x6 >= 75
  - 5*x0 + 21*x1 + 14*x6 >= 132
  - 5*x0 + 34*x4 + 14*x6 >= 132
  - 5*x0 + 21*x1 + 14*x2 + 27*x3 + 34*x4 + 18*x5 + 14*x6 >= 132

- Carbohydrates constraints:
  - 24*x0 + 14*x1 + 23*x2 + 21*x3 + 11*x4 + 12*x5 + 17*x6 >= 97
  - 24*x0 + 11*x4 >= 43
  - 14*x1 + 11*x4 >= 80
  - 21*x3 + 11*x4 >= 125
  - 14*x1 + 12*x5 >= 108
  - 23*x2 + 21*x3 >= 116
  - 11*x4 + 12*x5 >= 49
  - 14*x1 + 12*x5 + 17*x6 >= 123
  - 14*x1 + 21*x3 + 11*x4 >= 123
  - 24*x0 + 21*x3 + 17*x6 >= 123
  - 23*x2 + 11*x4 + 17*x6 >= 123
  - 14*x1 + 21*x3 + 12*x5 >= 123
  - 14*x1 + 11*x4 + 17*x6 >= 123
  - 24*x0 + 23*x2 + 17*x6 >= 123
  - 24*x0 + 11*x4 + 12*x5 >= 123
  - 24*x0 + 23*x2 + 11*x4 >= 123
  - 23*x2 + 21*x3 + 11*x4 >= 123
  - 24*x0 + 14*x1 + 23*x2 >= 123
  - 24*x0 + 21*x3 + 12*x5 >= 123
  - 24*x0 + 14*x1 + 17*x6 >= 123
  - 14*x1 + 23*x2 + 17*x6 >= 123
  - 14*x1 + 12*x5 + 17*x6 >= 110
  - 14*x1 + 21*x3 + 11*x4 >= 110
  - 24*x0 + 21*x3 + 17*x6 >= 110
  - 23*x2 + 11*x4 + 17*x6 >= 110
  - 14*x1 + 21*x3 + 12*x5 >= 110
  - 14*x1 + 23*x2 + 17*x6 >= 110
  - 24*x0 + 14*x1 + 23*x2 >= 110
  - 24*x0 + 21*x3 + 12*x5 >= 110
  - 24*x0 + 14*x1 + 17*x6 >= 110
  - 24*x0 + 23*x2 + 11*x4 >= 110
  - 23*x2 + 21*x3 + 11*x4 >= 110
  - 24*x0 + 23*x2 + 17*x6 >= 110
  - 23*x2 + 21*x3 + 17*x6 >= 110
  - 14*x1 + 12*x5 + 17*x6 >= 97
  - 14*x1 + 21*x3 + 11*x4 >= 97
  - 24*x0 + 21*x3 + 17*x6 >= 97
  - 23*x2 + 11*x4 + 17*x6 >= 97
  - 14*x1 + 21*x3 + 12*x5 >= 97
  - 14*x1 + 11*x4 + 17*x6 >= 97
  - 24*x0 + 23*x2 + 17*x6 >= 97
  - 24*x0 + 11*x4 + 12*x5 >= 97
  - 24*x0 + 23*x2 + 11*x4 >= 97
  - 23*x2 + 21*x3 + 11*x4 >= 97
  - 24*x0 + 14*x1 + 23*x2 >= 97
  - 24*x0 + 21*x3 + 12*x5 >= 97
  - 24*x0 + 14*x1 + 17*x6 >= 97
  - 14*x1 + 23*x2 + 17*x6 >= 97
  - 14*x1 + 12*x5 + 17*x6 >= 85
  - ... (similarly for other carbohydrates constraints)

- Umami index constraints:
  - 6*x0 + 26*x1 + 27*x2 + 8*x3 + 23*x4 + 18*x5 + 13*x6 >= 116
  - ... (similarly for other umami index constraints)

- Calcium constraints:
  - 7*x0 + 3*x1 + 17*x2 + 13*x3 + 26*x4 + 24*x5 + 3*x6 >= 129
  - ... (similarly for other calcium constraints)

## Step 5: Provide the symbolic representation in JSON format
```json
{
  "sym_variables": [
    ["x0", "bagged salads"],
    ["x1", "strips of bacon"],
    ["x2", "kiwis"],
    ["x3", "cornichons"],
    ["x4", "fruit salads"],
    ["x5", "bananas"],
    ["x6", "eggs"]
  ],
  "objective_function": "2.18*x0 + 5.08*x1 + 8.26*x2 + 8.58*x3 + 4.98*x4 + 5.83*x5 + 4.0*x6",
  "constraints": [
    "16*x0 + 18*x1 + 19*x2 + 15*x3 + 11*x4 + 10*x5 + 28*x6 >= 62",
    "5*x0 + 21*x1 + 14*x2 + 27*x3 + 34*x4 + 18*x5 + 14*x6 >= 113",
    "24*x0 + 14*x1 + 23*x2 + 21*x3 + 11*x4 + 12*x5 + 17*x6 >= 97",
    "6*x0 + 26*x1 + 27*x2 + 8*x3 + 23*x4 + 18*x5 + 13*x6 >= 116",
    "7*x0 + 3*x1 + 17*x2 + 13*x3 + 26*x4 + 24*x5 + 3*x6 >= 129"
  ]
}
```

## Step 6: Output Gurobi code
```python
import gurobi

def optimize_problem():
    model = gurobi.Model()

    # Define variables
    x0 = model.addVar(name="x0", lb=0)  # bagged salads
    x1 = model.addVar(name="x1", lb=0)  # strips of bacon
    x2 = model.addVar(name="x2", lb=0)  # kiwis
    x3 = model.addVar(name="x3", lb=0)  # cornichons
    x4 = model.addVar(name="x4", lb=0)  # fruit salads
    x5 = model.addVar(name="x5", lb=0)  # bananas
    x6 = model.addVar(name="x6", lb=0)  # eggs

    # Objective function
    model.setObjective(2.18*x0 + 5.08*x1 + 8.26*x2 + 8.58*x3 + 4.98*x4 + 5.83*x5 + 4.0*x6, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(16*x0 + 18*x1 + 19*x2 + 15*x3 + 11*x4 + 10*x5 + 28*x6 >= 62)
    model.addConstr(5*x0 + 21*x1 + 14*x2 + 27*x3 + 34*x4 + 18*x5 + 14*x6 >= 113)
    model.addConstr(24*x0 + 14*x1 + 23*x2 + 21*x3 + 11*x4 + 12*x5 + 17*x6 >= 97)
    model.addConstr(6*x0 + 26*x1 + 27*x2 + 8*x3 + 23*x4 + 18*x5 + 13*x6 >= 116)
    model.addConstr(7*x0 + 3*x1 + 17*x2 + 13*x3 + 26*x4 + 24*x5 + 3*x6 >= 129)

    # Add other constraints...

    model.optimize()

    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print("Objective value:", model.objVal)
        print("x0 (bagged salads):", x0.varValue)
        print("x1 (strips of bacon):", x1.varValue)
        print("x2 (kiwis):", x2.varValue)
        print("x3 (cornichons):", x3.varValue)
        print("x4 (fruit salads):", x4.varValue)
        print("x5 (bananas):", x5.varValue)
        print("x6 (eggs):", x6.varValue)
    else:
        print("No optimal solution found.")

optimize_problem()
```