## Step 1: Define the symbolic representation of the variables
The variables are ['lilies', 'boxwoods', 'vincas', 'chili plants', 'roses'] which can be represented symbolically as ['x1', 'x2', 'x3', 'x4', 'x5'].

## Step 2: Create a dictionary mapping symbolic variables to their natural language counterparts
The symbolic variables and their corresponding natural language objects are:
- x1: lilies
- x2: boxwoods
- x3: vincas
- x4: chili plants
- x5: roses

## Step 3: Define the objective function in symbolic notation
The objective function to minimize is: $1.84x_1 + 5.88x_2 + 5.84x_3 + 1.97x_4 + 6.9x_5$.

## 4: List all the constraints in symbolic notation
The constraints are:
1. $2x_1 + 13x_2 + 12x_3 + 14x_4 + 10x_5 \geq 157$ is not directly given, instead we have:
- $12x_3 + 10x_5 \geq 26$
- $2x_1 + 12x_3 \geq 11$
- $2x_1 + 13x_2 \geq 17$
- $13x_2 + 14x_4 \geq 30$
- $12x_3 + 14x_4 \geq 29$
- $13x_2 + 12x_3 \geq 15$
- $13x_2 + 12x_3 + 14x_4 \geq 21$
- $2x_1 + 13x_2 + 12x_3 + 14x_4 + 10x_5 \geq 21$
- $x_2 - 4x_4 \geq 0$
- $2x_3 - 10x_5 \geq 0$
- $2x_1 + 13x_2 \leq 117$
- $13x_2 + 10x_5 \leq 110$
- $2x_1 + 12x_3 + 10x_5 \leq 70$
- $13x_2 + 12x_3 + 10x_5 \leq 90$
- $2x_1 + 12x_3 + 14x_4 \leq 136$
- $2x_1 + 13x_2 + 10x_5 \leq 36$
- $12x_3 + 14x_4 + 10x_5 \leq 44$
- $2x_1 + 13x_2 + 14x_4 \leq 149$

## 5: Specify the symbolic representation of the problem
```json
{
'sym_variables': [('x1', 'lilies'), ('x2', 'boxwoods'), ('x3', 'vincas'), ('x4', 'chili plants'), ('x5', 'roses')],
'objective_function': '1.84*x1 + 5.88*x2 + 5.84*x3 + 1.97*x4 + 6.9*x5',
'constraints': [
    '12*x3 + 10*x5 >= 26',
    '2*x1 + 12*x3 >= 11',
    '2*x1 + 13*x2 >= 17',
    '13*x2 + 14*x4 >= 30',
    '12*x3 + 14*x4 >= 29',
    '13*x2 + 12*x3 >= 15',
    '13*x2 + 12*x3 + 14*x4 >= 21',
    '2*x1 + 13*x2 + 12*x3 + 14*x4 + 10*x5 >= 21',
    'x2 - 4*x4 >= 0',
    '2*x3 - 10*x5 >= 0',
    '2*x1 + 13*x2 <= 117',
    '13*x2 + 10*x5 <= 110',
    '2*x1 + 12*x3 + 10*x5 <= 70',
    '13*x2 + 12*x3 + 10*x5 <= 90',
    '2*x1 + 12*x3 + 14*x4 <= 136',
    '2*x1 + 13*x2 + 10*x5 <= 36',
    '12*x3 + 14*x4 + 10*x5 <= 44',
    '2*x1 + 13*x2 + 14*x4 <= 149'
  ]
}
```

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

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    lilies = model.addVar(name="lilies", vtype=gurobi.GRB.INTEGER)
    boxwoods = model.addVar(name="boxwoods", vtype=gurobi.GRB.INTEGER)
    vincas = model.addVar(name="vincas", vtype=gurobi.GRB.INTEGER)
    chili_plants = model.addVar(name="chili_plants", vtype=gurobi.GRB.INTEGER)
    roses = model.addVar(name="roses", vtype=gurobi.GRB.INTEGER)

    # Define the objective function
    model.setObjective(1.84 * lilies + 5.88 * boxwoods + 5.84 * vincas + 1.97 * chili_plants + 6.9 * roses, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(12 * vincas + 10 * roses >= 26)
    model.addConstr(2 * lilies + 12 * vincas >= 11)
    model.addConstr(2 * lilies + 13 * boxwoods >= 17)
    model.addConstr(13 * boxwoods + 14 * chili_plants >= 30)
    model.addConstr(12 * vincas + 14 * chili_plants >= 29)
    model.addConstr(13 * boxwoods + 12 * vincas >= 15)
    model.addConstr(13 * boxwoods + 12 * vincas + 14 * chili_plants >= 21)
    model.addConstr(2 * lilies + 13 * boxwoods + 12 * vincas + 14 * chili_plants + 10 * roses >= 21)
    model.addConstr(boxwoods - 4 * chili_plants >= 0)
    model.addConstr(2 * vincas - 10 * roses >= 0)
    model.addConstr(2 * lilies + 13 * boxwoods <= 117)
    model.addConstr(13 * boxwoods + 10 * roses <= 110)
    model.addConstr(2 * lilies + 12 * vincas + 10 * roses <= 70)
    model.addConstr(13 * boxwoods + 12 * vincas + 10 * roses <= 90)
    model.addConstr(2 * lilies + 12 * vincas + 14 * chili_plants <= 136)
    model.addConstr(2 * lilies + 13 * boxwoods + 10 * roses <= 36)
    model.addConstr(12 * vincas + 14 * chili_plants + 10 * roses <= 44)
    model.addConstr(2 * lilies + 13 * boxwoods + 14 * chili_plants <= 149)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objval)
        print("Lilies: ", lilies.varValue)
        print("Boxwoods: ", boxwoods.varValue)
        print("Vincas: ", vincas.varValue)
        print("Chili Plants: ", chili_plants.varValue)
        print("Roses: ", roses.varValue)
    else:
        print("The model is infeasible")

solve_optimization_problem()
```