To solve this optimization problem, we will first create a symbolic representation of the variables and the objective function. Then, we'll define the constraints based on the given information.

The variables in the problem are:
- 'black beans'
- 'steaks'

These can be represented symbolically as:
- x0 for 'black beans'
- x1 for 'steaks'

The objective function is to minimize: 1*x0 + 7*x1

The constraints based on the resources/attributes are:
- Iron constraint: 1*x0 + 9*x1 >= 18 (at least 18 milligrams of iron)
- Iron constraint upper bound: 1*x0 + 9*x1 <= 71 (at most 71 milligrams of iron)
- Cost constraint: 3*x0 + 7*x1 >= 22 (at least $22 spent)
- Cost constraint upper bound: 3*x0 + 7*x1 <= 45 (no more than $45 spent)
- Fiber constraint: 1*x0 + 3*x1 >= 11 (at least 11 grams of fiber)
- Fiber constraint upper bound: 1*x0 + 3*x1 <= 28 (no more than 28 grams of fiber)
- Additional constraint: 10*x0 - x1 >= 0

The symbolic representation of the problem is:
```json
{
    'sym_variables': [('x0', 'black beans'), ('x1', 'steaks')],
    'objective_function': 'minimize 1*x0 + 7*x1',
    'constraints': [
        '1*x0 + 9*x1 >= 18',
        '1*x0 + 9*x1 <= 71',
        '3*x0 + 7*x1 >= 22',
        '3*x0 + 7*x1 <= 45',
        '1*x0 + 3*x1 >= 11',
        '1*x0 + 3*x1 <= 28',
        '10*x0 - x1 >= 0'
    ]
}
```

To solve this problem using Gurobi, we need to define the model, variables, objective function, and constraints.

```python
from gurobipy import *

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

# Define the variables
x0 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="black_beans")
x1 = m.addVar(lb=0, vtype=GRB.INTEGER, name="steaks")

# Set the objective function
m.setObjective(1*x0 + 7*x1, GRB.MINIMIZE)

# Add constraints
m.addConstr(1*x0 + 9*x1 >= 18, "iron_constraint")
m.addConstr(1*x0 + 9*x1 <= 71, "iron_upper_bound")
m.addConstr(3*x0 + 7*x1 >= 22, "cost_constraint")
m.addConstr(3*x0 + 7*x1 <= 45, "cost_upper_bound")
m.addConstr(1*x0 + 3*x1 >= 11, "fiber_constraint")
m.addConstr(1*x0 + 3*x1 <= 28, "fiber_upper_bound")
m.addConstr(10*x0 - x1 >= 0, "additional_constraint")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Black beans: {x0.x}")
    print(f"Steaks: {x1.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```