## Step 1: Define the symbolic representation of the variables
The variables are 'pickles', 'hot dogs', and 'kale salads', which can be represented symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language description into a symbolic representation
The symbolic representation of the variables is:
- $x_0$ : pickles
- $x_1$ : hot dogs
- $x_2$ : kale salads

## 3: Define the objective function in symbolic notation
The objective function to minimize is $7x_0 + 2x_1 + 3x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $14x_1 + 17x_2 \geq 30$ (total combined healthiness rating from hot dogs and kale salads)
- $25x_0 + 17x_2 \geq 18$ (total combined healthiness rating from pickles and kale salads)
- $25x_0 + 14x_1 \geq 18$ (total combined healthiness rating from pickles and hot dogs)
- $25x_0 + 14x_1 + 17x_2 \geq 34$ (total combined healthiness rating from pickles, hot dogs, and kale salads)
- $16x_0 + 7x_1 \geq 90$ (total combined grams of protein from pickles and hot dogs)
- $-x_1 + 8x_2 \geq 0$ (constraint involving hot dogs and kale salads)
- $25x_0 + 14x_1 \leq 67$ (total combined healthiness rating from pickles and hot dogs)
- $16x_0 + 7x_1 + 14x_2 \leq 263$ (total combined grams of protein from pickles, hot dogs, and kale salads)
- $7x_1 + 14x_2 \geq 49$ (total combined grams of protein from hot dogs and kale salads)
- $16x_0 + 7x_1 + 14x_2 \geq 90$ (total combined grams of protein from pickles, hot dogs, and kale salads)

## 5: Create the Gurobi model and variables
We will now create a Gurobi model and define the variables.

## 6: Implement the objective function and constraints in Gurobi
```python
import gurobi

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

# Define the variables
x0 = model.addVar(name="pickles", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
x1 = model.addVar(name="hot_dogs", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
x2 = model.addVar(name="kale_salads", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)

# Set the objective function
model.setObjective(7 * x0 + 2 * x1 + 3 * x2, gurobi.GRB.MINIMIZE)

# Add constraints
model.addConstr(14 * x1 + 17 * x2 >= 30)
model.addConstr(25 * x0 + 17 * x2 >= 18)
model.addConstr(25 * x0 + 14 * x1 >= 18)
model.addConstr(25 * x0 + 14 * x1 + 17 * x2 >= 34)
model.addConstr(16 * x0 + 7 * x1 >= 90)
model.addConstr(-x1 + 8 * x2 >= 0)
model.addConstr(25 * x0 + 14 * x1 <= 67)
model.addConstr(16 * x0 + 7 * x1 + 14 * x2 <= 263)
model.addConstr(7 * x1 + 14 * x2 >= 49)
model.addConstr(16 * x0 + 7 * x1 + 14 * x2 >= 90)

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", model.objval)
    print("Pickles: ", x0.varValue)
    print("Hot Dogs: ", x1.varValue)
    print("Kale Salads: ", x2.varValue)
else:
    print("The model is infeasible")
```

## 7: Provide the symbolic representation in JSON format
```json
{
    "sym_variables": [
        ["x0", "pickles"],
        ["x1", "hot dogs"],
        ["x2", "kale salads"]
    ],
    "objective_function": "7*x0 + 2*x1 + 3*x2",
    "constraints": [
        "14*x1 + 17*x2 >= 30",
        "25*x0 + 17*x2 >= 18",
        "25*x0 + 14*x1 >= 18",
        "25*x0 + 14*x1 + 17*x2 >= 34",
        "16*x0 + 7*x1 >= 90",
        "-x1 + 8*x2 >= 0",
        "25*x0 + 14*x1 <= 67",
        "16*x0 + 7*x1 + 14*x2 <= 263",
        "7*x1 + 14*x2 >= 49",
        "16*x0 + 7*x1 + 14*x2 >= 90"
    ]
}
```